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16723b73-0822-4de5-acb9-6660190d99ba
|
deeply-supervised-depth-map-super-resolution
|
1808.08688
| null |
http://arxiv.org/abs/1808.08688v1
|
http://arxiv.org/pdf/1808.08688v1.pdf
|
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
|
Deep convolutional neural network (DCNN) has been successfully applied to
depth map super-resolution and outperforms existing methods by a wide margin.
However, there still exist two major issues with these DCNN based depth map
super-resolution methods that hinder the performance: i) The low-resolution
depth maps either need to be up-sampled before feeding into the network or
substantial deconvolution has to be used; and ii) The supervision
(high-resolution depth maps) is only applied at the end of the network, thus it
is difficult to handle large up-sampling factors, such as $\times 8, \times
16$. In this paper, we propose a new framework to tackle the above problems.
First, we propose to represent the task of depth map super-resolution as a
series of novel view synthesis sub-tasks. The novel view synthesis sub-task
aims at generating (synthesizing) a depth map from different camera pose, which
could be learned in parallel. Second, to handle large up-sampling factors, we
present a deeply supervised network structure to enforce strong supervision in
each stage of the network. Third, a multi-scale fusion strategy is proposed to
effectively exploit the feature maps at different scales and handle the
blocking effect. In this way, our proposed framework could deal with
challenging depth map super-resolution efficiently under large up-sampling
factors (e.g. $\times 8, \times 16$). Our method only uses the low-resolution
depth map as input, and the support of color image is not needed, which greatly
reduces the restriction of our method. Extensive experiments on various
benchmarking datasets demonstrate the superiority of our method over current
state-of-the-art depth map super-resolution methods.
|
['Xibin Song', 'Xueying Qin', 'Yuchao Dai']
|
2018-08-27
| null | null | null | null |
['depth-map-super-resolution']
|
['computer-vision']
|
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[10.100486755371094, -2.305907726287842]
|
2620a631-fc18-4f69-a212-2a1c9427bce6
|
multi30k-multilingual-english-german-image
|
1605.00459
| null |
http://arxiv.org/abs/1605.00459v1
|
http://arxiv.org/pdf/1605.00459v1.pdf
|
Multi30K: Multilingual English-German Image Descriptions
|
We introduce the Multi30K dataset to stimulate multilingual multimodal
research. Recent advances in image description have been demonstrated on
English-language datasets almost exclusively, but image description should not
be limited to English. This dataset extends the Flickr30K dataset with i)
German translations created by professional translators over a subset of the
English descriptions, and ii) descriptions crowdsourced independently of the
original English descriptions. We outline how the data can be used for
multilingual image description and multimodal machine translation, but we
anticipate the data will be useful for a broader range of tasks.
|
["Khalil Sima'an", 'Lucia Specia', 'Stella Frank', 'Desmond Elliott']
|
2016-05-02
|
multi30k-multilingual-english-german-image-1
|
https://aclanthology.org/W16-3210
|
https://aclanthology.org/W16-3210.pdf
|
ws-2016-8
|
['multimodal-machine-translation']
|
['natural-language-processing']
|
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|
[11.265400886535645, 1.488726258277893]
|
927fa572-d670-4d2e-9d66-0f7da542253c
|
learning-semantic-aware-knowledge-guidance
|
2304.07039
| null |
https://arxiv.org/abs/2304.07039v1
|
https://arxiv.org/pdf/2304.07039v1.pdf
|
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
|
Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.
|
['Heng Tao Shen', 'Chongyi Li', 'Jiwei Wei', 'Yang Yang', 'Guoqing Wang', 'Chen Pan', 'Yuhui Wu']
|
2023-04-14
| null |
http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.html
|
http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Learning_Semantic-Aware_Knowledge_Guidance_for_Low-Light_Image_Enhancement_CVPR_2023_paper.pdf
|
cvpr-2023-1
|
['image-enhancement', 'low-light-image-enhancement']
|
['computer-vision', 'computer-vision']
|
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|
[10.67158031463623, -2.463547945022583]
|
fb7cb994-3184-497f-9c94-fca4b92016a3
|
r-theta-local-neighborhood-pattern-for
|
2201.00504
| null |
https://arxiv.org/abs/2201.00504v1
|
https://arxiv.org/pdf/2201.00504v1.pdf
|
R-Theta Local Neighborhood Pattern for Unconstrained Facial Image Recognition and Retrieval
|
In this paper R-Theta Local Neighborhood Pattern (RTLNP) is proposed for facial image retrieval. RTLNP exploits relationships amongst the pixels in local neighborhood of the reference pixel at different angular and radial widths. The proposed encoding scheme divides the local neighborhood into sectors of equal angular width. These sectors are again divided into subsectors of two radial widths. Average grayscales values of these two subsectors are encoded to generate the micropatterns. Performance of the proposed descriptor has been evaluated and results are compared with the state of the art descriptors e.g. LBP, LTP, CSLBP, CSLTP, Sobel-LBP, LTCoP, LMeP, LDP, LTrP, MBLBP, BRINT and SLBP. The most challenging facial constrained and unconstrained databases, namely; AT&T, CARIA-Face-V5-Cropped, LFW, and Color FERET have been used for showing the efficiency of the proposed descriptor. Proposed descriptor is also tested on near infrared (NIR) face databases; CASIA NIR-VIS 2.0 and PolyU-NIRFD to explore its potential with respect to NIR facial images. Better retrieval rates of RTLNP as compared to the existing state of the art descriptors show the effectiveness of the descriptor
|
['Pavan Chakraborty', 'Satish Kumar Singh', 'Soumendu Chakraborty']
|
2022-01-03
| null | null | null | null |
['face-image-retrieval']
|
['computer-vision']
|
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|
[12.99657154083252, 0.6564406752586365]
|
871b4373-fc82-4801-b590-ec5ec041d4a6
|
a-comparative-study-on-end-to-end-speech-to
|
1911.0887
| null |
https://arxiv.org/abs/1911.08870v1
|
https://arxiv.org/pdf/1911.08870v1.pdf
|
A Comparative Study on End-to-end Speech to Text Translation
|
Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an overview of different end-to-end architectures, as well as the usage of an auxiliary connectionist temporal classification (CTC) loss for better convergence. We also investigate on pre-training variants such as initializing different components of a model using pre-trained models, and their impact on the final performance, which gives boosts up to 4% in BLEU and 5% in TER. Our experiments are performed on 270h IWSLT TED-talks En->De, and 100h LibriSpeech Audiobooks En->Fr. We also show improvements over the current end-to-end state-of-the-art systems on both tasks.
|
['Tobias Bieschke', 'Hermann Ney', 'Parnia Bahar']
|
2019-11-20
| null | null | null | null |
['speech-to-text-translation']
|
['natural-language-processing']
|
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|
[14.481297492980957, 7.212531566619873]
|
81410ace-9a28-45d0-887d-094adf019a44
|
opentag-open-attribute-value-extraction-from
|
1806.01264
| null |
http://arxiv.org/abs/1806.01264v2
|
http://arxiv.org/pdf/1806.01264v2.pdf
|
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
|
Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted features. How can we discover new attribute values that we have
never seen before? Can we do this with limited human annotation or supervision?
We study this problem in the context of product catalogs that often have
missing values for many attributes of interest.
In this work, we leverage product profile information such as titles and
descriptions to discover missing values of product attributes. We develop a
novel deep tagging model OpenTag for this extraction problem with the following
contributions: (1) we formalize the problem as a sequence tagging task, and
propose a joint model exploiting recurrent neural networks (specifically,
bidirectional LSTM) to capture context and semantics, and Conditional Random
Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention
mechanism to provide interpretable explanation for our model's decisions, (3)
we propose a novel sampling strategy exploring active learning to reduce the
burden of human annotation. OpenTag does not use any dictionary or hand-crafted
features as in prior works. Extensive experiments in real-life datasets in
different domains show that OpenTag with our active learning strategy discovers
new attribute values from as few as 150 annotated samples (reduction in 3.3x
amount of annotation effort) with a high F-score of 83%, outperforming
state-of-the-art models.
|
['Fei-Fei Li', 'Xin Luna Dong', 'Subhabrata Mukherjee', 'Guineng Zheng']
|
2018-06-01
| null | null | null | null |
['attribute-value-extraction']
|
['natural-language-processing']
|
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|
[10.010843276977539, 6.387849807739258]
|
39801e3c-4506-4e6c-a89f-d4e7249c4a8b
|
maximizing-spatio-temporal-entropy-of-deep-3d
|
2303.02693
| null |
https://arxiv.org/abs/2303.02693v1
|
https://arxiv.org/pdf/2303.02693v1.pdf
|
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
|
3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences, leading to cubically growing and expensive computations. To reduce the computational cost, previous methods resort to manually designed 3D/2D CNN structures with approximations or automatic search, which sacrifice the modeling ability or make training time-consuming. In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity. To measure the expressiveness of 3D CNNs efficiently, we formulate a 3D CNN as an information system and derive an analytic entropy score, based on the Maximum Entropy Principle. Specifically, we propose a spatio-temporal entropy score (STEntr-Score) with a refinement factor to handle the discrepancy of visual information in spatial and temporal dimensions, through dynamically leveraging the correlation between the feature map size and kernel size depth-wisely. Highly efficient and expressive 3D CNN architectures, \ie entropy-based 3D CNNs (E3D family), can then be efficiently searched by maximizing the STEntr-Score under a given computational budget, via an evolutionary algorithm without training the network parameters. Extensive experiments on Something-Something V1\&V2 and Kinetics400 demonstrate that the E3D family achieves state-of-the-art performance with higher computational efficiency. Code is available at https://github.com/alibaba/lightweight-neural-architecture-search.
|
['Yang song', 'Maurice Pagnucco', 'Ming Lin', 'Xiuyu Sun', 'Dong Gong', 'Yichen Qian', 'Zhenhong Sun', 'Junyan Wang']
|
2023-03-05
| null | null | null | null |
['video-recognition']
|
['computer-vision']
|
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|
[8.682124137878418, 2.89023756980896]
|
99decd12-09c9-4cb1-922c-9b8abfc07188
|
region-based-temporally-consistent-video-post
| null | null |
http://openaccess.thecvf.com/content_cvpr_2015/html/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2015/papers/Dong_Region-Based_Temporally_Consistent_2015_CVPR_paper.pdf
|
Region-Based Temporally Consistent Video Post-Processing
|
We study the problem of temporally consistent video post-processing. Previous post-processing algorithms usually either fail to keep high fidelity or fail to keep temporal consistency of output videos. In this paper, we observe experimentally that many image/video enhancement algorithms enforce a spatially consistent prior on the enhancement. More precisely, within a local region, the enhancement is consistent, i.e., pixels with the same RGB values will get the same enhancement values. Using this prior, we segment each frame into several regions and temporally-spatially adjust the enhancement of regions of different frames, taking into account fidelity, temporal consistency and spatial consistency. User study, objective measurement and visual quality comparisons are conducted. The experimental results demonstrate that our output videos can keep high fidelity and temporal consistency at the same time.
|
['Boyan Bonev', 'Xuan Dong', 'Alan L. Yuille', 'Yu Zhu']
|
2015-06-01
| null | null | null |
cvpr-2015-6
|
['video-enhancement']
|
['computer-vision']
|
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|
[11.038710594177246, -1.7889527082443237]
|
6d3d2172-a5fd-48a5-9d43-c7262691d786
|
semeval-2019-task-1-cross-lingual-semantic
|
1903.02953
| null |
https://arxiv.org/abs/1903.02953v3
|
https://arxiv.org/pdf/1903.02953v3.pdf
|
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
|
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task's website \url{https://competitions.codalab.org/competitions/19160}.
|
['Zohar Aizenbud', 'Omri Abend', 'Leshem Choshen', 'Elior Sulem', 'Daniel Hershcovich', 'Ari Rappoport']
|
2019-03-06
|
semeval-2019-task-1-cross-lingual-semantic-1
|
https://aclanthology.org/S19-2001
|
https://aclanthology.org/S19-2001.pdf
|
semeval-2019-6
|
['ucca-parsing']
|
['natural-language-processing']
|
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|
[10.434858322143555, 9.553281784057617]
|
7d69089c-0e61-4d6e-a752-09aa5af2152b
|
walking-for-short-distances-and-turning-in
|
1909.03139
| null |
https://arxiv.org/abs/1909.03139v3
|
https://arxiv.org/pdf/1909.03139v3.pdf
|
Walking for short distances and turning in lower-limb amputees: a study in low-cost prosthesis users
|
Preferred walking speed is a widely-used performance measure for people with mobility issues, but is usually measured in straight line walking for fixed distances or durations. However, daily walking involves walking for bouts of different distances and walking with turning. Here, we studied walking for short distances and walking in circles in unilateral lower-limb amputees wearing an above or below-knee passive prosthesis, specifically, a Jaipur foot prosthesis. Analogous to earlier results in non-amputees, we found that their preferred walking speeds are lower for short distances and lower for circles of smaller radii. Using inverse optimization, we estimated the cost of changing speeds and turning such that the observed preferred walking speeds in our experiments minimizes the total energy cost. The inferred costs of changing speeds and turning were much larger than for non-amputees. These findings could inform prosthesis design and rehabilitation therapy to better assist changing speeds and turning tasks in amputee walking. Further, measuring the preferred speed for a range of distances and radii is a more robust subject-specific measure of walking performance.
|
['Manoj Srinivasan', 'Anil Kumar Jain', 'Nidhi Seethapathi']
|
2019-09-06
| null | null | null | null |
['total-energy']
|
['miscellaneous']
|
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|
[6.973666191101074, 0.2417665272951126]
|
570a8327-997e-48dd-9613-ba97598227d7
|
zero-shot-text-to-parameter-translation-for
|
2303.01311
| null |
https://arxiv.org/abs/2303.01311v1
|
https://arxiv.org/pdf/2303.01311v1.pdf
|
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
|
Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-game characters. Previous in-game character auto-creation systems are mostly image-driven, where facial parameters are optimized so that the rendered character looks similar to the reference face photo. This paper proposes a novel text-to-parameter translation method (T2P) to achieve zero-shot text-driven game character auto-creation. With our method, users can create a vivid in-game character with arbitrary text description without using any reference photo or editing hundreds of parameters manually. In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework. Due to the discontinuous parameter representation, previous methods have difficulty in effectively learning discrete facial parameters. T2P, to our best knowledge, is the first method that can handle the optimization of both discrete and continuous parameters. Experimental results show that T2P can generate high-quality and vivid game characters with given text prompts. T2P outperforms other SOTA text-to-3D generation methods on both objective evaluations and subjective evaluations.
|
['Changjie Fan', 'Zhenwei Shi', 'Zhengxia Zou', 'Lincheng Li', 'Zhipeng Hu', 'Wei Li', 'Rui Zhao']
|
2023-03-02
| null |
http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.html
|
http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Zero-Shot_Text-to-Parameter_Translation_for_Game_Character_Auto-Creation_CVPR_2023_paper.pdf
|
cvpr-2023-1
|
['face-model', 'text-to-3d']
|
['computer-vision', 'computer-vision']
|
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|
[12.242745399475098, -0.5779370069503784]
|
4b66a8ad-ae5f-4e09-9a86-6841646cca30
|
robust-scheduling-with-gflownets
|
2302.05446
| null |
https://arxiv.org/abs/2302.05446v2
|
https://arxiv.org/pdf/2302.05446v2.pdf
|
Robust Scheduling with GFlowNets
|
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimize proxy metrics, which are fast to evaluate but can lead to bad schedules when tested on the target hardware. In this work, we propose a new approach to scheduling by sampling proportionally to the proxy metric using a novel GFlowNet method. We introduce a technique to control the trade-off between diversity and goodness of the proposed schedules at inference time and demonstrate empirically that the pure optimization baselines can lead to subpar performance with respect to our approach when tested on a target model. Furthermore, we show that conditioning the GFlowNet on the computation graph enables generalization to unseen scheduling problems for both synthetic and real-world compiler datasets.
|
['Roberto Bondesan', 'Markus Peschl', 'Corrado Rainone', 'David W. Zhang']
|
2023-01-17
| null | null | null | null |
['compiler-optimization']
|
['computer-code']
|
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|
[7.712677001953125, 7.337266445159912]
|
2bbdf899-29cc-4dd0-b675-4c7e260f8364
|
marvin-semantic-annotation-using-multiple
|
1602.00515
| null |
http://arxiv.org/abs/1602.00515v2
|
http://arxiv.org/pdf/1602.00515v2.pdf
|
Marvin: Semantic annotation using multiple knowledge sources
|
People are producing more written material then anytime in the history. The
increase is so high that professionals from the various fields are no more able
to cope with this amount of publications. Text mining tools can offer tools to
help them and one of the tools that can aid information retrieval and
information extraction is semantic text annotation. In this report we present
Marvin, a text annotator written in Java, which can be used as a command line
tool and as a Java library. Marvin is able to annotate text using multiple
sources, including WordNet, MetaMap, DBPedia and thesauri represented as SKOS.
|
['Nikola Milosevic']
|
2016-02-01
| null | null | null | null |
['text-annotation']
|
['natural-language-processing']
|
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|
[9.378661155700684, 8.554664611816406]
|
6607d1fa-3324-4ab6-857d-5fa3f78acd6f
|
causal-knowledge-extraction-from-scholarly
|
2006.08904
| null |
https://arxiv.org/abs/2006.08904v1
|
https://arxiv.org/pdf/2006.08904v1.pdf
|
Causal Knowledge Extraction from Scholarly Papers in Social Sciences
|
The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge. In this paper, we seek to develop natural language processing (NLP) models to accelerate the speed of extraction of relationships from scholarly papers in social sciences, identify hypotheses from these papers, and extract the cause-and-effect entities. Specifically, we develop models to 1) classify sentences in scholarly documents in business and management as hypotheses (hypothesis classification), 2) classify these hypotheses as causal relationships or not (causality classification), and, if they are causal, 3) extract the cause and effect entities from these hypotheses (entity extraction). We have achieved high performance for all the three tasks using different modeling techniques. Our approach may be generalizable to scholarly documents in a wide range of social sciences, as well as other types of textual materials.
|
['Felipe Montano-Campos', 'Victor Zitian Chen', 'Wlodek Zadrozny']
|
2020-06-16
| null | null | null | null |
['entity-extraction']
|
['natural-language-processing']
|
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|
[9.583416938781738, 8.39196491241455]
|
43c5c869-a428-47c1-b0b7-aa029078448e
|
hdr-image-reconstruction-from-a-single
|
1710.0748
| null |
http://arxiv.org/abs/1710.07480v1
|
http://arxiv.org/pdf/1710.07480v1.pdf
|
HDR image reconstruction from a single exposure using deep CNNs
|
Camera sensors can only capture a limited range of luminance simultaneously,
and in order to create high dynamic range (HDR) images a set of different
exposures are typically combined. In this paper we address the problem of
predicting information that have been lost in saturated image areas, in order
to enable HDR reconstruction from a single exposure. We show that this problem
is well-suited for deep learning algorithms, and propose a deep convolutional
neural network (CNN) that is specifically designed taking into account the
challenges in predicting HDR values. To train the CNN we gather a large dataset
of HDR images, which we augment by simulating sensor saturation for a range of
cameras. To further boost robustness, we pre-train the CNN on a simulated HDR
dataset created from a subset of the MIT Places database. We demonstrate that
our approach can reconstruct high-resolution visually convincing HDR results in
a wide range of situations, and that it generalizes well to reconstruction of
images captured with arbitrary and low-end cameras that use unknown camera
response functions and post-processing. Furthermore, we compare to existing
methods for HDR expansion, and show high quality results also for image based
lighting. Finally, we evaluate the results in a subjective experiment performed
on an HDR display. This shows that the reconstructed HDR images are visually
convincing, with large improvements as compared to existing methods.
|
['Rafał K. Mantiuk', 'Gyorgy Denes', 'Gabriel Eilertsen', 'Jonas Unger', 'Joel Kronander']
|
2017-10-20
| null | null | null | null |
['hdr-reconstruction']
|
['computer-vision']
|
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|
[10.956131935119629, -2.2258191108703613]
|
ace29415-d820-439c-8507-4b503147b304
|
detecting-layout-templates-in-complex
|
2109.0663
| null |
https://arxiv.org/abs/2109.06630v2
|
https://arxiv.org/pdf/2109.06630v2.pdf
|
Detecting Layout Templates in Complex Multiregion Files
|
Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems that practitioners face is the presence of multiple, independent regions in a single spreadsheet, possibly separated by repeated empty cells. We define such files as "multiregion" files. In collections of various spreadsheets, we can observe that some share the same layout. We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions. Our approach is composed of three phases: first, each file is rendered as an image and inspected for elements that could form regions; then, using a clustering algorithm, the identified elements are grouped to form regions; finally, every file layout is represented as a graph and compared with others to find layout templates. We compare our method to state-of-the-art table recognition algorithms on two corpora of real-world enterprise spreadsheets. Our approach shows the best performances in detecting reliable region boundaries within each file and can correctly identify recurring layouts across files.
|
['Felix Naumann', 'Lan Jiang', 'Gerardo Vitagliano']
|
2021-09-14
| null | null | null | null |
['table-recognition']
|
['computer-vision']
|
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|
[11.699625968933105, 2.8550570011138916]
|
d010662a-5729-4c7a-940c-2b6f6a496311
|
aifb-webscience-at-semeval-2022-task-12-1
| null | null |
https://aclanthology.org/2022.semeval-1.232
|
https://aclanthology.org/2022.semeval-1.232.pdf
|
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities
|
In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in sequence, our system incorporates information from relation extraction into entity extraction. This means that the system can be trained even on data sets where only a subset of all valid entity spans is annotated. We provide an extensive evaluation of the proposed system and its strengths and weaknesses. Our approach, which can be scaled dynamically in computational complexity at inference time, produces predictions with high precision and reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the domain of physics and math, it achieves high relation extraction macro F1 scores of 95.43% and 79.17%, respectively. The code used for training and evaluating our models is available at: https://github.com/nicpopovic/RE1st
|
['Michael Färber', 'Walter Laurito', 'Nicholas Popovic']
| null | null | null | null |
semeval-naacl-2022-7
|
['joint-entity-and-relation-extraction']
|
['natural-language-processing']
|
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|
[9.496164321899414, 8.791851043701172]
|
22a51be0-ede9-485c-a774-1aa187642b8e
|
neighborhood-random-walk-graph-sampling-for
|
2112.07743
| null |
https://arxiv.org/abs/2112.07743v1
|
https://arxiv.org/pdf/2112.07743v1.pdf
|
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
|
In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been to use a Bayesian framework and view the graph as a random variable with probabilities associated with model parameters. Introducing the Bayesian paradigm to graph-based models, specifically for semi-supervised node classification, has been shown to yield higher classification accuracies. However, the method of graph inference proposed in recent work does not take into account the structure of the graph. In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS), which uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
|
['Justin Zhan', 'Aneesh Komanduri']
|
2021-12-14
| null | null | null | null |
['graph-sampling']
|
['graphs']
|
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-6.69457018e-01 2.52665579e-01 8.39349926e-01 1.10270989e+00
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|
[7.009309768676758, 5.705138683319092]
|
dba0864f-d637-409e-bb61-525cc42e5113
|
spatiotemporal-recurrent-convolutional
|
1901.04656
| null |
http://arxiv.org/abs/1901.04656v1
|
http://arxiv.org/pdf/1901.04656v1.pdf
|
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions
|
Recently, the recognition task of spontaneous facial micro-expressions has
attracted much attention with its various real-world applications. Plenty of
handcrafted or learned features have been employed for a variety of classifiers
and achieved promising performances for recognizing micro-expressions. However,
the micro-expression recognition is still challenging due to the subtle
spatiotemporal changes of micro-expressions. To exploit the merits of deep
learning, we propose a novel deep recurrent convolutional networks based
micro-expression recognition approach, capturing the spatial-temporal
deformations of micro-expression sequence. Specifically, the proposed deep
model is constituted of several recurrent convolutional layers for extracting
visual features and a classificatory layer for recognition. It is optimized by
an end-to-end manner and obviates manual feature design. To handle sequential
data, we exploit two types of extending the connectivity of convolutional
networks across temporal domain, in which the spatiotemporal deformations are
modeled in views of facial appearance and geometry separately. Besides, to
overcome the shortcomings of limited and imbalanced training samples, temporal
data augmentation strategies as well as a balanced loss are jointly used for
our deep network. By performing the experiments on three spontaneous
micro-expression datasets, we verify the effectiveness of our proposed
micro-expression recognition approach compared to the state-of-the-art methods.
|
['Xiaoyi Feng', 'Xiaopeng Hong', 'Zhaoqiang Xia', 'Xingyu Gao', 'Guoying Zhao']
|
2019-01-15
| null | null | null | null |
['micro-expression-recognition']
|
['computer-vision']
|
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|
[13.640079498291016, 1.7153239250183105]
|
4a60849a-6fd1-4ba7-b57c-346890f1ee93
|
vision-transformer-using-low-level-chest-x
|
2104.07235
| null |
https://arxiv.org/abs/2104.07235v1
|
https://arxiv.org/pdf/2104.07235v1.pdf
|
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
|
Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficult to collect under the global COVID-19 pandemic. On the other hand, CXR data with other findings are abundant. This situation is ideally suited for the Vision Transformer (ViT) architecture, where a lot of unlabeled data can be used through structural modeling by the self-attention mechanism. However, the use of existing ViT is not optimal, since feature embedding through direct patch flattening or ResNet backbone in the standard ViT is not intended for CXR. To address this problem, here we propose a novel Vision Transformer that utilizes low-level CXR feature corpus obtained from a backbone network that extracts common CXR findings. Specifically, the backbone network is first trained with large public datasets to detect common abnormal findings such as consolidation, opacity, edema, etc. Then, the embedded features from the backbone network are used as corpora for a Transformer model for the diagnosis and the severity quantification of COVID-19. We evaluate our model on various external test datasets from totally different institutions to evaluate the generalization capability. The experimental results confirm that our model can achieve the state-of-the-art performance in both diagnosis and severity quantification tasks with superior generalization capability, which are sine qua non of widespread deployment.
|
['Jong Chul Ye', 'Jae-Kwang Lim', 'Sungjun Moon', 'Jin Hwan Kim', 'Sang Min Lee', 'Joon Beom Seo', 'Yujin Oh', 'Gwanghyun Kim', 'Sangjoon Park']
|
2021-04-15
| null | null | null | null |
['covid-19-detection']
|
['medical']
|
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|
[15.40383529663086, -1.8434216976165771]
|
b9323536-d5b0-42a6-8401-89d0e466f9ae
|
ieee-802-11ad-based-joint-radar-communication
|
2209.04235
| null |
https://arxiv.org/abs/2209.04235v1
|
https://arxiv.org/pdf/2209.04235v1.pdf
|
IEEE 802.11ad Based Joint Radar Communication Transceiver: Design, Prototype and Performance Analysis
|
Rapid beam alignment is required to support high gain millimeter wave (mmW) communication links between a base station (BS) and mobile users (MU). The standard IEEE 802.11ad protocol enables beam alignment at the BS and MU through a lengthy beam training procedure accomplished through additional packet overhead. However, this results in reduced latency and throughput. Auxiliary radar functionality embedded within the communication protocol has been proposed in prior literature to enable rapid beam alignment of communication beams without the requirement of channel overheads. In this work, we propose a complete architectural framework of a joint radar-communication wireless transceiver wherein radar based detection of MU is realized to enable subsequent narrow beam communication. We provide a software prototype implementation with transceiver design details, signal models and signal processing algorithms. The prototype is experimentally evaluated with realistic simulations in free space and Rician propagation conditions and demonstrated to accelerate the beam alignment by a factor of four while reducing the overall bit error rate (BER) resulting in significant improvement in throughput with respect to standard 802.11ad. Likewise, the radar performance is found to be comparable to commonly used mmW radars.
|
['Sumit Darak', 'Shobha Sundar Ram', 'V Sri Sindhu', 'Soumya Jain', 'Akanksha Sneh']
|
2022-09-09
| null | null | null | null |
['joint-radar-communication']
|
['robots']
|
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3.70456547e-01 -6.88582897e-01 -1.91823915e-01 -2.43093684e-01]
|
[6.358428955078125, 1.2277685403823853]
|
8db1bb3f-e7b4-47e2-8268-a09f72966327
|
pvn3d-a-deep-point-wise-3d-keypoints-voting
|
1911.04231
| null |
https://arxiv.org/abs/1911.04231v2
|
https://arxiv.org/pdf/1911.04231v2.pdf
|
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
|
In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner. Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It allows us to fully utilize the geometric constraint of rigid objects with the extra depth information and is easy for a network to learn and optimize. Extensive experiments were conducted to demonstrate the effectiveness of 3D-keypoint detection in the 6D pose estimation task. Experimental results also show our method outperforms the state-of-the-art methods by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/PVN3D.git.
|
['Yisheng He', 'Wei Sun', 'Haibin Huang', 'Jian Sun', 'Jianran Liu', 'Haoqiang Fan']
|
2019-11-11
|
pvn3d-a-deep-point-wise-3d-keypoints-voting-1
|
http://openaccess.thecvf.com/content_CVPR_2020/html/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.pdf
|
cvpr-2020-6
|
['6d-pose-estimation-using-rgbd']
|
['computer-vision']
|
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-1.03722423e-01 3.86345536e-01 -3.91504109e-01 -1.05854094e+00
-6.73754334e-01 -6.24281168e-01 -1.33054793e+00 -9.20158923e-02
-5.59284925e-01 -3.18311840e-01 9.83270586e-01 7.38584518e-01
2.76038200e-01 5.53429499e-02 7.14002728e-01 -1.58166230e+00
-4.98676687e-01 -6.48314357e-01 -3.52541298e-01 8.84412229e-02
5.66483855e-01 -8.54329884e-01 -3.90402228e-01 -2.39552826e-01]
|
[7.409302234649658, -2.5497939586639404]
|
abf8daa0-08cc-496b-bb17-622ab64e66be
|
using-shortlists-to-support-decision-making
|
1510.07545
| null |
http://arxiv.org/abs/1510.07545v2
|
http://arxiv.org/pdf/1510.07545v2.pdf
|
Using Shortlists to Support Decision Making and Improve Recommender System Performance
|
In this paper, we study shortlists as an interface component for recommender
systems with the dual goal of supporting the user's decision process, as well
as improving implicit feedback elicitation for increased recommendation
quality. A shortlist is a temporary list of candidates that the user is
currently considering, e.g., a list of a few movies the user is currently
considering for viewing. From a cognitive perspective, shortlists serve as
digital short-term memory where users can off-load the items under
consideration -- thereby decreasing their cognitive load. From a machine
learning perspective, adding items to the shortlist generates a new implicit
feedback signal as a by-product of exploration and decision making which can
improve recommendation quality. Shortlisting therefore provides additional data
for training recommendation systems without the increases in cognitive load
that requesting explicit feedback would incur.
We perform an user study with a movie recommendation setup to compare
interfaces that offer shortlist support with those that do not. From the user
studies we conclude: (i) users make better decisions with a shortlist; (ii)
users prefer an interface with shortlist support; and (iii) the additional
implicit feedback from sessions with a shortlist improves the quality of
recommendations by nearly a factor of two.
|
['Thorsten Joachims', 'Paul N. Bennett', 'Tobias Schnabel', 'Susan T. Dumais']
|
2015-10-26
| null | null | null | null |
['movie-recommendation']
|
['miscellaneous']
|
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-7.94757485e-01 6.53715074e-01 5.62156916e-01 8.37401927e-01
6.17280483e-01 6.56262875e-01 4.85099614e-01 -7.53392100e-01
-5.42951107e-01 -1.09771025e+00 -6.13686144e-01 4.63081598e-01
-6.02659248e-02 -8.79549742e-01 -1.74889460e-01 -5.29385023e-02]
|
[10.07044506072998, 5.730792999267578]
|
f6479500-86f5-40a8-80b0-3ff589e51908
|
spatiotemporal-besov-priors-for-bayesian
|
2306.16378
| null |
https://arxiv.org/abs/2306.16378v1
|
https://arxiv.org/pdf/2306.16378v1.pdf
|
Spatiotemporal Besov Priors for Bayesian Inverse Problems
|
Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many applications in the data science seek spatiotemporal reconstruction from a sequence of time-dependent objects with discontinuity or singularity, e.g. dynamic computerized tomography (CT) images with edges. Traditional methods based on Gaussian processes (GP) may not provide satisfactory solutions since they tend to offer over-smooth prior candidates. Recently, Besov process (BP) defined by wavelet expansions with random coefficients has been proposed as a more appropriate prior for this type of Bayesian inverse problems. While BP outperforms GP in imaging analysis to produce edge-preserving reconstructions, it does not automatically incorporate temporal correlation inherited in the dynamically changing images. In this paper, we generalize BP to the spatiotemporal domain (STBP) by replacing the random coefficients in the series expansion with stochastic time functions following Q-exponential process which governs the temporal correlation strength. Mathematical and statistical properties about STBP are carefully studied. A white-noise representation of STBP is also proposed to facilitate the point estimation through maximum a posterior (MAP) and the uncertainty quantification (UQ) by posterior sampling. Two limited-angle CT reconstruction examples and a highly non-linear inverse problem involving Navier-Stokes equation are used to demonstrate the advantage of the proposed STBP in preserving spatial features while accounting for temporal changes compared with the classic STGP and a time-uncorrelated approach.
|
['Shuyi Li', 'Mirjeta Pasha', 'Shiwei Lan']
|
2023-06-28
| null | null | null | null |
['gaussian-processes']
|
['methodology']
|
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|
[12.373048782348633, -2.586965799331665]
|
46eb6743-cb29-4fa9-8e60-ee6e9397ff65
|
mishape-3d-shape-modelling-of-mitochondria-in
|
2303.01546
| null |
https://arxiv.org/abs/2303.01546v1
|
https://arxiv.org/pdf/2303.01546v1.pdf
|
MiShape: 3D Shape Modelling of Mitochondria in Microscopy
|
Fluorescence microscopy is a quintessential tool for observing cells and understanding the underlying mechanisms of life-sustaining processes of all living organisms. The problem of extracting 3D shape of mitochondria from fluorescence microscopy images remains unsolved due to the complex and varied shapes expressed by mitochondria and the poor resolving capacity of these microscopes. We propose an approach to bridge this gap by learning a shape prior for mitochondria termed as MiShape, by leveraging high-resolution electron microscopy data. MiShape is a generative model learned using implicit representations of mitochondrial shapes. It provides a shape distribution that can be used to generate infinite realistic mitochondrial shapes. We demonstrate the representation power of MiShape and its utility for 3D shape reconstruction given a single 2D fluorescence image or a small 3D stack of 2D slices. We also showcase applications of our method by deriving simulated fluorescence microscope datasets that have realistic 3D ground truths for the problem of 2D segmentation and microscope-to-microscope transformation.
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['Dilip K. Prasad', 'Krishna Agarwal', 'Alexander Horsch', 'Suyog S Jadhav', 'Abhinanda R. Punnakkal']
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2023-03-02
| null | null | null | null |
['3d-shape-reconstruction']
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['computer-vision']
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|
[13.527907371520996, -3.0119881629943848]
|
199806fd-76dc-46fe-af9d-b65c14dd3c25
|
multimodal-emotion-recognition-using-deep
|
1908.05349
| null |
https://arxiv.org/abs/1908.05349v1
|
https://arxiv.org/pdf/1908.05349v1.pdf
|
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
|
Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition. The basic idea behind DCCA is to transform each modality separately and coordinate different modalities into a hyperspace by using specified canonical correlation analysis constraints. We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition accuracy rates on all five datasets: 94.58% on the SEED dataset, 87.45% on the SEED-IV dataset, 84.33% and 85.62% for two binary classification tasks and 88.51% for a four-category classification task on the DEAP dataset, 83.08% on the SEED-V dataset, and 88.99%, 90.57%, and 90.67% for three binary classification tasks on the DREAMER dataset. We also compare the noise robustness of DCCA with that of existing methods when adding various amounts of noise to the SEED-V dataset. The experimental results indicate that DCCA has greater robustness. By visualizing feature distributions with t-SNE and calculating the mutual information between different modalities before and after using DCCA, we find that the features transformed by DCCA from different modalities are more homogeneous and discriminative across emotions.
|
['Bao-liang Lu', 'Wei-Long Zheng', 'Jie-Lin Qiu', 'Wei Liu']
|
2019-08-13
| null | null | null | null |
['multimodal-emotion-recognition', 'multimodal-emotion-recognition']
|
['computer-vision', 'speech']
|
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|
[13.215713500976562, 5.095531463623047]
|
d3860726-615e-4927-9174-e006ed50dc00
|
memory-based-gaze-prediction-in-deep
|
2202.04877
| null |
https://arxiv.org/abs/2202.04877v1
|
https://arxiv.org/pdf/2202.04877v1.pdf
|
Memory-based gaze prediction in deep imitation learning for robot manipulation
|
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning to robot manipulation have been limited to reactive control based on the states at the current time step. However, future robots will also be required to solve tasks utilizing their memory obtained by experience in complicated environments (e.g., when the robot is asked to find a previously used object on a shelf). In such a situation, simple deep imitation learning may fail because of distractions caused by complicated environments. We propose that gaze prediction from sequential visual input enables the robot to perform a manipulation task that requires memory. The proposed algorithm uses a Transformer-based self-attention architecture for the gaze estimation based on sequential data to implement memory. The proposed method was evaluated with a real robot multi-object manipulation task that requires memory of the previous states.
|
['Yasuo Kuniyoshi', 'Yoshiyuki Ohmura', 'Heecheol Kim']
|
2022-02-10
| null | null | null | null |
['gaze-estimation', 'eye-tracking', 'robot-manipulation']
|
['computer-vision', 'computer-vision', 'robots']
|
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|
[4.637472152709961, 0.8795510530471802]
|
9e7d7dfd-f4ec-4813-9e36-7a43dd12f07d
|
hydra-hgr-a-hybrid-transformer-based
|
2211.02619
| null |
https://arxiv.org/abs/2211.02619v1
|
https://arxiv.org/pdf/2211.02619v1.pdf
|
HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information
|
Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-based architectures that perform Hand Gesture Recognition (HGR) at a macroscopic level (i.e., directly from sEMG signals). At the same time, advancements in acquisition of High-Density sEMG signals (HD-sEMG) have resulted in a surge of significant interest on sEMG decomposition techniques to extract microscopic neural drive information. However, due to complexities of sEMG decomposition and added computational overhead, HGR at microscopic level is less explored than its aforementioned DNN-based counterparts. In this regard, we propose the HYDRA-HGR framework, which is a hybrid model that simultaneously extracts a set of temporal and spatial features through its two independent Vision Transformer (ViT)-based parallel architectures (the so called Macro and Micro paths). The Macro Path is trained directly on the pre-processed HD-sEMG signals, while the Micro path is fed with the p-to-p values of the extracted Motor Unit Action Potentials (MUAPs) of each source. Extracted features at macroscopic and microscopic levels are then coupled via a Fully Connected (FC) fusion layer. We evaluate the proposed hybrid HYDRA-HGR framework through a recently released HD-sEMG dataset, and show that it significantly outperforms its stand-alone counterparts. The proposed HYDRA-HGR framework achieves average accuracy of 94.86% for the 250 ms window size, which is 5.52% and 8.22% higher than that of the Macro and Micro paths, respectively.
|
['Arash Mohammadi', 'Hamid Alinejad-Rokny', 'S. Farokh Atashzar', 'Farnoosh Naderkhani', 'Elahe Rahimian', 'Mansooreh Montazerin']
|
2022-10-27
| null | null | null | null |
['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition']
|
['computer-vision', 'computer-vision', 'computer-vision']
|
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|
[6.83968448638916, 0.1446067839860916]
|
df5fda06-47e6-4085-866e-0643d69d1ee1
|
multi-candidate-word-segmentation-using-bi
| null | null |
https://ieeexplore.ieee.org/abstract/document/8442053
|
https://ieeexplore.ieee.org/abstract/document/8442053
|
Multi-Candidate Word Segmentation using Bi-directional LSTM Neural Networks
|
Most existing word segmentation methods output one single segmentation solution. This paper provides an analysis of word segmentation performance when more than one solutions are taken into account. Towards this investigation, a deep neural network with multiple thresholds is applied to generate multiple candidates for segmentation. As a test-bed, the well-known bidirectional long short-term memory (BiLSTM) units are used with eleven contexts in a deep neural network. As performance indices, three measures; recall, precision and f-measure, are plotted with respect to various thresholds for both boundary level and word level evaluation. By a number of experiments, the result shows that the multi-candidate word segmentation can help us increase the recalls while maintaining the precisions.
|
['Thanaruk Theeramunkong', 'Kobkrit Viriyayudhakom', 'Theerapat Lapjaturapit']
|
2018-05-07
| null | null | null | null |
['thai-word-tokenization']
|
['natural-language-processing']
|
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|
[10.080302238464355, 10.153451919555664]
|
f1abd6b7-2f51-47dc-a1f1-9994327320ad
|
self-educated-language-agent-with-hindsight
| null | null |
https://openreview.net/forum?id=S1g_t1StDB
|
https://openreview.net/pdf?id=S1g_t1StDB
|
Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following
|
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. These properties make it a natural fit to guide the training of interactive agents as it could ease recurrent challenges in Reinforcement Learning such as sample complexity, generalization, or multi-tasking. Yet, it remains an open-problem to relate language and RL in even simple instruction following scenarios. Current methods rely on expert demonstrations, auxiliary losses, or inductive biases in neural architectures. In this paper, we propose an orthogonal approach called Textual Hindsight Experience Replay (THER) that extends the Hindsight Experience Replay approach to the language setting. Whenever the agent does not fulfill its instruction, THER learn to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, THER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We observe that this simple idea also initiates a learning synergy between language acquisition and policy learning on instruction following tasks in the BabyAI environment.
|
['Olivier Pietquin', 'Florian Strub', 'Mathieu Seurin', 'Geoffrey Cideron']
|
2019-09-25
| null | null | null | null |
['language-acquisition']
|
['natural-language-processing']
|
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|
[4.230926513671875, 1.3628730773925781]
|
5de3ceee-d902-47e4-a407-7c7c9443825a
|
a-new-dimension-in-testimony-relighting-video
|
2104.02773
| null |
https://arxiv.org/abs/2104.02773v1
|
https://arxiv.org/pdf/2104.02773v1.pdf
|
A New Dimension in Testimony: Relighting Video with Reflectance Field Exemplars
|
We present a learning-based method for estimating 4D reflectance field of a person given video footage illuminated under a flat-lit environment of the same subject. For training data, we use one light at a time to illuminate the subject and capture the reflectance field data in a variety of poses and viewpoints. We estimate the lighting environment of the input video footage and use the subject's reflectance field to create synthetic images of the subject illuminated by the input lighting environment. We then train a deep convolutional neural network to regress the reflectance field from the synthetic images. We also use a differentiable renderer to provide feedback for the network by matching the relit images with the input video frames. This semi-supervised training scheme allows the neural network to handle unseen poses in the dataset as well as compensate for the lighting estimation error. We evaluate our method on the video footage of the real Holocaust survivors and show that our method outperforms the state-of-the-art methods in both realism and speed.
|
['Paul Debevec', 'Bipin Kishore', 'Loc Huynh']
|
2021-04-06
| null | null | null | null |
['lighting-estimation']
|
['computer-vision']
|
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|
[9.7579984664917, -2.92212176322937]
|
408a35c2-d508-455e-a290-f9ce062d840d
|
face-parsing-via-a-fully-convolutional
|
1708.03736
| null |
http://arxiv.org/abs/1708.03736v1
|
http://arxiv.org/pdf/1708.03736v1.pdf
|
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
|
In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CNN integrates three subnetworks, a unary network, a pairwise network
and a continuous Conditional Random Field (C-CRF) network into a unified
framework. The high-level semantic information and low-level details across
different convolutional layers are captured by the convolutional and
deconvolutional structures in the unary network. The semantic edge context is
learnt by the pairwise network branch to construct pixel-wise affinity. Based
on a differentiable superpixel pooling layer and a differentiable C-CRF layer,
the unary network and pairwise network are combined via a novel continuous CRF
network to achieve spatial consistency in both training and test procedure of a
deep neural network. Comprehensive evaluations on LFW-PL and HELEN datasets
demonstrate that FC-CNN achieves better performance over the other
state-of-arts for accurate face labeling on challenging images.
|
['Lei Zhou', 'Xiangjian He', 'Zhi Liu']
|
2017-08-12
| null | null | null | null |
['face-parsing']
|
['computer-vision']
|
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|
[13.432294845581055, 0.6481053829193115]
|
a26c3641-26d9-4a91-aab5-17f887c76313
|
micro-expression-spotting-a-benchmark
|
1710.0282
| null |
http://arxiv.org/abs/1710.02820v1
|
http://arxiv.org/pdf/1710.02820v1.pdf
|
Micro-Expression Spotting: A Benchmark
|
Micro-expressions are rapid and involuntary facial expressions, which
indicate the suppressed or concealed emotions. Recently, the research on
automatic micro-expression (ME) spotting obtains increasing attention. ME
spotting is a crucial step prior to further ME analysis tasks. The spotting
results can be used as important cues to assist many other human-oriented tasks
and thus have many potential applications. In this paper, by investigating
existing ME spotting methods, we recognize the immediacy of standardizing the
performance evaluation of micro-expression spotting methods. To this end, we
construct a micro-expression spotting benchmark (MESB). Firstly, we set up a
sliding window based multi-scale evaluation framework. Secondly, we introduce a
series of protocols. Thirdly, we also provide baseline results of popular
methods. The MESB facilitates the research on ME spotting with fairer and more
comprehensive evaluation and also enables to leverage the cutting-edge machine
learning tools widely.
|
['Thuong-Khanh Tran', 'Xiaopeng Hong', 'Guoying Zhao']
|
2017-10-08
| null | null | null | null |
['micro-expression-spotting']
|
['computer-vision']
|
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|
[13.60830020904541, 1.7750904560089111]
|
c15c7aab-dbe0-4672-936b-26694cd79cae
|
compositional-transformers-for-scene-1
| null | null |
http://proceedings.neurips.cc/paper/2021/hash/4eff0720836a198b6174eecf02cbfdbf-Abstract.html
|
http://proceedings.neurips.cc/paper/2021/file/4eff0720836a198b6174eecf02cbfdbf-Paper.pdf
|
Compositional Transformers for Scene Generation
|
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture. Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2's strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency. Further experiments demonstrate the model's disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects' depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes. See https://github.com/dorarad/gansformer for model implementation.
|
['Larry Zitnick', 'Dor Arad Hudson']
|
2021-12-01
| null | null | null |
neurips-2021-12
|
['scene-generation']
|
['computer-vision']
|
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|
[11.334733963012695, -0.36042335629463196]
|
b7392f13-a773-4a74-9338-dd5b734ced91
|
crossing-the-line-where-do-demographic
| null | null |
https://aclanthology.org/2020.acl-srw.24
|
https://aclanthology.org/2020.acl-srw.24.pdf
|
Crossing the Line: Where do Demographic Variables Fit into Humor Detection?
|
Recent humor classification shared tasks have struggled with two issues: either the data comprises a highly constrained genre of humor which does not broadly represent humor, or the data is so indiscriminate that the inter-annotator agreement on its humor content is drastically low. These tasks typically average over all annotators{'} judgments, in spite of the fact that humor is a highly subjective phenomenon. We argue that demographic factors influence whether a text is perceived as humorous or not. We propose the addition of demographic information about the humor annotators in order to bin ratings more sensibly. We also suggest the addition of an {`}offensive{'} label to distinguish between different generations, in terms of humor. This would allow for more nuanced shared tasks and could lead to better performance on downstream tasks, such as content moderation.
|
['J. A. Meaney']
|
2020-07-01
| null | null | null |
acl-2020-6
|
['humor-detection']
|
['natural-language-processing']
|
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|
[8.893218994140625, 11.047408103942871]
|
49ad9567-d91f-4c07-b74f-d534ce28c2d9
|
utilizing-a-transparency-driven-environment
|
1810.00968
| null |
http://arxiv.org/abs/1810.00968v1
|
http://arxiv.org/pdf/1810.00968v1.pdf
|
Utilizing a Transparency-driven Environment toward Trusted Automatic Genre Classification: A Case Study in Journalism History
|
With the growing abundance of unlabeled data in real-world tasks, researchers
have to rely on the predictions given by black-boxed computational models.
However, it is an often neglected fact that these models may be scoring high on
accuracy for the wrong reasons. In this paper, we present a practical impact
analysis of enabling model transparency by various presentation forms. For this
purpose, we developed an environment that empowers non-computer scientists to
become practicing data scientists in their own research field. We demonstrate
the gradually increasing understanding of journalism historians through a
real-world use case study on automatic genre classification of newspaper
articles. This study is a first step towards trusted usage of machine learning
pipelines in a responsible way.
|
['Marcel Broersma', 'Kim Smeenk', 'Erik Tjong Kim Sang', 'Laura Hollink', 'Frank Harbers', 'Jacco van Ossenbruggen', 'Aysenur Bilgin']
|
2018-10-01
| null | null | null | null |
['genre-classification']
|
['computer-vision']
|
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|
[9.583174705505371, 7.885828018188477]
|
f7e24290-a453-4e1e-8d3f-89e337914e83
|
accelerating-markov-random-field-inference
|
2108.0057
| null |
https://arxiv.org/abs/2108.00570v1
|
https://arxiv.org/pdf/2108.00570v1.pdf
|
Accelerating Markov Random Field Inference with Uncertainty Quantification
|
Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on generating random numbers from probability distributions. These algorithms are computationally expensive on conventional processors, yet their statistical properties, namely interpretability and uncertainty quantification (UQ) compared to deep learning, make them an attractive alternative approach. Therefore, hardware specialization can be adopted to address the shortcomings of conventional processors in running these applications. In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling. We propose a tiled architecture which takes advantage of near-memory computing, and memory optimizations tailored to the semantics of MRF. Additionally, we propose a novel hybrid on-chip/off-chip memory system and logging scheme to efficiently support UQ. This memory system design is not specific to MRF models and is applicable to applications using probabilistic algorithms. In addition, it dramatically reduces off-chip memory bandwidth requirements. We implemented an FPGA prototype of our proposed architecture using high-level synthesis tools and achieved 146MHz frequency for an accelerator with 32 function units on an Intel Arria 10 FPGA. Compared to prior work on FPGA, our accelerator achieves 26X speedup. Furthermore, our proposed memory system and logging scheme to support UQ reduces off-chip bandwidth by 71% for two applications. ASIC analysis in 15nm shows our design with 2048 function units running at 3GHz outperforms GPU implementations of motion estimation and stereo vision on Nvidia RTX2080Ti by 120X-210X, occupying only 7.7% of the area.
|
['Alvin R. Lebeck', 'Sayan Mukherjee', 'Xiangyu Zhang', 'Ramin Bashizade']
|
2021-08-02
| null | null | null | null |
['2048']
|
['playing-games']
|
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|
[8.384272575378418, 2.9874823093414307]
|
42ae0a25-53cc-4d0d-83e3-f43073869ab9
|
how-does-truth-evolve-into-fake-news-an
|
2103.05944
| null |
https://arxiv.org/abs/2103.05944v1
|
https://arxiv.org/pdf/2103.05944v1.pdf
|
How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution
|
Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, the continuous evolution process would generate unprecedented fake news and cheat the original model. We present the Fake News Evolution (FNE) dataset: a new dataset tracking the fake news evolution process. Our dataset is composed of 950 paired data, each of which consists of articles representing the three significant phases of the evolution process, which are the truth, the fake news, and the evolved fake news. We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.
|
['Rui Yan', 'Dongyan Zhao', 'Juntao Li', 'Xiuying Chen', 'Mingfei Guo']
|
2021-03-10
| null | null | null | null |
['deception-detection']
|
['miscellaneous']
|
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|
[8.107641220092773, 10.285042762756348]
|
2a36cd21-a293-49e9-93ad-980f2ee2f643
|
video-saliency-detection-with-domain-adaption
|
2010.0122
| null |
https://arxiv.org/abs/2010.01220v4
|
https://arxiv.org/pdf/2010.01220v4.pdf
|
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
|
In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction levels. We provide the base hierarchical learning mechanism with two techniques for domain adaptation and domain-specific learning. For the former, we encourage the model to unsupervisedly learn hierarchical general features using gradient reversal at multiple scales, to enhance generalization capabilities on datasets for which no annotations are provided during training. As for domain specialization, we employ domain-specific operations (namely, priors, smoothing and batch normalization) by specializing the learned features on individual datasets in order to maximize performance. The results of our experiments show that the proposed model yields state-of-the-art accuracy on supervised saliency prediction. When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two. When, instead, we test it in an unsupervised domain adaptation setting, by enabling hierarchical gradient reversal layers, we obtain performance comparable to supervised state-of-the-art.
|
['Concetto Spampinato', 'Daniela Giordano', 'Francesco Rundo', 'Simone Palazzo', 'Federica Proietto Salanitri', 'Giovanni Bellitto']
|
2020-10-02
| null | null | null | null |
['video-saliency-detection']
|
['computer-vision']
|
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|
[9.780314445495605, 1.1588507890701294]
|
b1768e1d-9f24-4eea-ab99-e58dfe1c6f15
|
preventing-dimensional-collapse-of-incomplete
|
2303.12241
| null |
https://arxiv.org/abs/2303.12241v1
|
https://arxiv.org/pdf/2303.12241v1.pdf
|
Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning
|
Incomplete multi-view clustering (IMVC) is an unsupervised approach, among which IMVC via contrastive learning has received attention due to its excellent performance. The previous methods have the following problems: 1) Over-reliance on additional projection heads when solving the dimensional collapse problem in which latent features are only valid in lower-dimensional subspaces during clustering. However, many parameters in the projection heads are unnecessary. 2) The recovered view contain inconsistent private information and useless private information will mislead the learning of common semantics due to consistent learning and reconstruction learning on the same feature. To address the above issues, we propose a novel incomplete multi-view contrastive clustering framework. This framework directly optimizes the latent feature subspace, utilizes the learned feature vectors and their sub-vectors for reconstruction learning and consistency learning, thereby effectively avoiding dimensional collapse without relying on projection heads. Since reconstruction loss and contrastive loss are performed on different features, the adverse effect of useless private information is reduced. For the incomplete data, the missing information is recovered by the cross-view prediction mechanism and the inconsistent information from different views is discarded by the minimum conditional entropy to further avoid the influence of private information. Extensive experimental results of the method on 5 public datasets show that the method achieves state-of-the-art clustering results.
|
['Shengxia Gao', 'Baokai Liu', 'Shiqiang Du', 'Kaiwu Zhang']
|
2023-03-22
| null | null | null | null |
['incomplete-multi-view-clustering']
|
['computer-vision']
|
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-5.53448379e-01 2.44555637e-01 2.14336306e-01 7.70945609e-01
4.65497792e-01 -2.10314710e-02 1.26016092e+00 -3.53258997e-01
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2.11261019e-01 -6.62111580e-01 -7.07235992e-01 1.18711889e-01]
|
[8.322790145874023, 4.601260185241699]
|
3db167b3-9f79-4c18-b0f6-d1420ab90235
|
on-training-instance-selection-for-few-shot
|
2107.03176
| null |
https://arxiv.org/abs/2107.03176v1
|
https://arxiv.org/pdf/2107.03176v1.pdf
|
On Training Instance Selection for Few-Shot Neural Text Generation
|
Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. We hope that this work will call for more attention on this largely unexplored area.
|
['Vera Demberg', 'Hui-Syuan Yeh', 'Xiaoyu Shen', 'Ernie Chang']
|
2021-07-07
| null |
https://aclanthology.org/2021.acl-short.2
|
https://aclanthology.org/2021.acl-short.2.pdf
|
acl-2021-5
|
['data-to-text-generation']
|
['natural-language-processing']
|
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|
[11.684992790222168, 8.831450462341309]
|
831bed45-ec89-4bf4-a5fd-4cf3a089cf22
|
comparing-causal-frameworks-potential
|
2306.14351
| null |
https://arxiv.org/abs/2306.14351v1
|
https://arxiv.org/pdf/2306.14351v1.pdf
|
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
|
The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM. A key result then shows that every RCM -- including those that violate algebraic principles implied by the SCM framework -- emerges as an abstraction of some representable RCM. Finally, we illustrate the power of this ameliorative perspective by pinpointing an important role for SCM principles in classic applications of RCMs; conversely, we offer a characterization of the algebraic constraints implied by a graph, helping to substantiate further comparisons between the two frameworks.
|
['Thomas Icard', 'Duligur Ibeling']
|
2023-06-25
| null | null | null | null |
['causal-inference', 'causal-inference']
|
['knowledge-base', 'miscellaneous']
|
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|
[8.179605484008789, 5.779160976409912]
|
be1f3b80-d1b5-4497-9c69-7329946ca91b
|
emrkbqa-a-clinical-knowledge-base-question
| null | null |
https://aclanthology.org/2021.bionlp-1.7
|
https://aclanthology.org/2021.bionlp-1.7.pdf
|
emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
|
We present emrKBQA, a dataset for answering physician questions from a structured patient record. It consists of questions, logical forms and answers. The questions and logical forms are generated based on real-world physician questions and are slot-filled and answered from patients in the MIMIC-III KB through a semi-automated process. This community-shared release consists of over 940000 question, logical form and answer triplets with 389 types of questions and ~7.5 paraphrases per question type. We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.
|
['Peter Szolovits', 'Rachita Chandra', 'Diwakar Mahajan', 'Jennifer J Liang', 'Preethi Raghavan']
| null | null | null | null |
naacl-bionlp-2021-6
|
['clinical-knowledge', 'knowledge-base-question-answering']
|
['miscellaneous', 'natural-language-processing']
|
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|
[8.827226638793945, 8.490957260131836]
|
dff793b0-323d-42cb-810d-95965b9f34af
|
exploring-the-political-agenda-of-the
|
1607.03055
| null |
http://arxiv.org/abs/1607.03055v1
|
http://arxiv.org/pdf/1607.03055v1.pdf
|
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
|
This study analyzes the political agenda of the European Parliament (EP)
plenary, how it has evolved over time, and the manner in which Members of the
European Parliament (MEPs) have reacted to external and internal stimuli when
making plenary speeches. To unveil the plenary agenda and detect latent themes
in legislative speeches over time, MEP speech content is analyzed using a new
dynamic topic modeling method based on two layers of Non-negative Matrix
Factorization (NMF). This method is applied to a new corpus of all English
language legislative speeches in the EP plenary from the period 1999-2014. Our
findings suggest that two-layer NMF is a valuable alternative to existing
dynamic topic modeling approaches found in the literature, and can unveil niche
topics and associated vocabularies not captured by existing methods.
Substantively, our findings suggest that the political agenda of the EP evolves
significantly over time and reacts to exogenous events such as EU Treaty
referenda and the emergence of the Euro-crisis. MEP contributions to the
plenary agenda are also found to be impacted upon by voting behaviour and the
committee structure of the Parliament.
|
['James P. Cross', 'Derek Greene']
|
2016-07-11
| null | null | null | null |
['dynamic-topic-modeling']
|
['natural-language-processing']
|
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|
[8.969391822814941, 9.875978469848633]
|
0f494ee0-7c13-47aa-8549-ad82f3a48009
|
mlp-air-an-efficient-mlp-based-method-for
|
2304.08803
| null |
https://arxiv.org/abs/2304.08803v1
|
https://arxiv.org/pdf/2304.08803v1.pdf
|
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition
|
The task of Group Activity Recognition (GAR) aims to predict the activity category of the group by learning the actor spatial-temporal interaction relation in the group. Therefore, an effective actor relation learning method is crucial for the GAR task. The previous works mainly learn the interaction relation by the well-designed GCNs or Transformers. For example, to infer the actor interaction relation, GCNs need a learnable adjacency, and Transformers need to calculate the self-attention. Although the above methods can model the interaction relation effectively, they also increase the complexity of the model (the number of parameters and computations). In this paper, we design a novel MLP-based method for Actor Interaction Relation learning (MLP-AIR) in GAR. Compared with GCNs and Transformers, our method has a competitive but conceptually and technically simple alternative, significantly reducing the complexity. Specifically, MLP-AIR includes three sub-modules: MLP-based Spatial relation modeling module (MLP-S), MLP-based Temporal relation modeling module (MLP-T), and MLP-based Relation refining module (MLP-R). MLP-S is used to model the spatial relation between different actors in each frame. MLP-T is used to model the temporal relation between different frames for each actor. MLP-R is used further to refine the relation between different dimensions of relation features to improve the feature's expression ability. To evaluate the MLP-AIR, we conduct extensive experiments on two widely used benchmarks, including the Volleyball and Collective Activity datasets. Experimental results demonstrate that MLP-AIR can get competitive results but with low complexity.
|
['Jianqin Yin', 'Guoliang Xu']
|
2023-04-18
| null | null | null | null |
['group-activity-recognition']
|
['computer-vision']
|
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|
[8.361783027648926, 0.6954715847969055]
|
d21a44a9-4b5d-48a9-a6b1-2bbc38dbc2cf
|
dad-3dheads-a-large-scale-dense-accurate-and
|
2204.03688
| null |
https://arxiv.org/abs/2204.03688v2
|
https://arxiv.org/pdf/2204.03688v2.pdf
|
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
|
We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that accurately represent 3D head shape compared to the ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset, learns shape, expression, and pose parameters, and performs 3D reconstruction of a FLAME mesh. The model also incorporates a landmark prediction branch to take advantage of rich supervision and co-training of multiple related tasks. Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Landmarks Estimation on DAD-3DHeads dataset. Finally, the diversity of DAD-3DHeads in camera angles, facial expressions, and occlusions enables a benchmark to study in-the-wild generalization and robustness to distribution shifts. The dataset webpage is https://p.farm/research/dad-3dheads.
|
['Jiři Matas', 'Viktoriia Sharmanska', 'Igor Krashenyi', 'Yana Kurlyak', 'Orest Kupyn', 'Tetiana Martyniuk']
|
2022-04-07
| null |
http://openaccess.thecvf.com//content/CVPR2022/html/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.html
|
http://openaccess.thecvf.com//content/CVPR2022/papers/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.pdf
|
cvpr-2022-1
|
['head-pose-estimation']
|
['computer-vision']
|
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4.34476435e-01 -1.03340816e+00 -3.69404078e-01 -2.55636930e-01]
|
[13.519784927368164, 0.17936888337135315]
|
e7e34d59-8e30-4002-90a0-1a0dbe54ac83
|
breast-cancer-detection-and-diagnosis-a
|
2305.19937
| null |
https://arxiv.org/abs/2305.19937v1
|
https://arxiv.org/pdf/2305.19937v1.pdf
|
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures
|
Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low, with only 40% of diagnosed patients surviving beyond five years. The inadequate availability of resources, including qualified pathologists, delayed diagnoses, and ineffective therapy planning, contribute to this low survival rate. To address this pressing issue, medical specialists and researchers have turned to domain-specific AI approaches, specifically deep learning models, to develop end-to-end solutions that can be integrated into computer-aided diagnosis (CAD) systems. By improving the workflow of pathologists, these AI models have the potential to enhance the detection and diagnosis of breast cancer. This research focuses on evaluating the performance of various cutting-edge convolutional neural network (CNN) architectures in comparison to a relatively new model called the Vision Trans-former (ViT). The objective is to determine the superiority of these models in terms of their accuracy and effectiveness. The experimental results reveal that the ViT models outperform the other selected state-of-the-art CNN architectures, achieving an impressive accuracy rate of 95.15%. This study signifies a significant advancement in the field, as it explores the utilization of data augmentation and other relevant preprocessing techniques in conjunction with deep learning models for the detection and diagnosis of breast cancer using datasets of Breast Cancer Histopathological Image Classification.
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['Absalom E. Ezugwu', 'Brennon Maistry']
|
2023-05-31
| null | null | null | null |
['breast-cancer-detection', 'breast-cancer-detection', 'histopathological-image-classification']
|
['knowledge-base', 'medical', 'medical']
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|
[15.260746955871582, -2.773632526397705]
|
0b10d844-b56e-4015-9ad8-9cea6550d33d
|
towards-two-view-6d-object-pose-estimation-a
|
2207.0026
| null |
https://arxiv.org/abs/2207.00260v1
|
https://arxiv.org/pdf/2207.00260v1.pdf
|
Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy
|
Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods generally achieve less competitive results than RGBD-based methods, which deploy both image features and 3D structure features. To narrow down this performance gap, this paper proposes a framework for 6D object pose estimation that learns implicit 3D information from 2 RGB images. Combining the learned 3D information and 2D image features, we establish more stable correspondence between the scene and the object models. To seek for the methods best utilizing 3D information from RGB inputs, we conduct an investigation on three different approaches, including Early- Fusion, Mid-Fusion, and Late-Fusion. We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation. The experiments show that our method outperforms state-of-the-art RGB-based methods, and achieves comparable results with RGBD-based methods.
|
['Rong Xiong', 'Yue Wang', 'Lilu Liu', 'Jun Wu']
|
2022-07-01
| null | null | null | null |
['6d-pose-estimation']
|
['computer-vision']
|
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|
[7.453056335449219, -2.596479892730713]
|
414f7a73-575e-48ee-a9e4-eebe6eeb1411
|
learning-of-frequency-time-attention
|
2111.03258
| null |
https://arxiv.org/abs/2111.03258v2
|
https://arxiv.org/pdf/2111.03258v2.pdf
|
Learning of Time-Frequency Attention Mechanism for Automatic Modulation Recognition
|
Recent learning-based image classification and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation mode recognition, this paper proposes a time-frequency attention mechanism for a convolutional neural network (CNN)-based modulation recognition framework. The proposed time-frequency attention module is designed to learn which channel, frequency and time information is more meaningful in CNN for modulation recognition. We analyze the effectiveness of the proposed time-frequency attention mechanism and compare the proposed method with two existing learning-based methods. Experiments on an open-source modulation recognition dataset show that the recognition performance of the proposed framework is better than those of the framework without time-frequency attention and existing learning-based methods.
|
['Yi Gong', 'Yuan Zeng', 'Shangao Lin']
|
2021-11-05
| null | null | null | null |
['automatic-modulation-recognition']
|
['time-series']
|
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|
[6.504977226257324, 1.4932231903076172]
|
2a772f93-17ec-4b00-8288-d9e48dd1cd61
|
semi-supervised-batch-active-learning-via
|
2010.09654
| null |
https://arxiv.org/abs/2010.09654v1
|
https://arxiv.org/pdf/2010.09654v1.pdf
|
Semi-supervised Batch Active Learning via Bilevel Optimization
|
Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.
|
['Andreas Krause', 'Marco Tagliasacchi', 'Zalán Borsos']
|
2020-10-19
| null | null | null | null |
['data-summarization']
|
['miscellaneous']
|
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|
[9.583967208862305, 4.327975273132324]
|
6367d9a8-22b3-4de9-9e14-c7dbccde831e
|
xiaoicesing-2-a-high-fidelity-singing-voice
|
2210.14666
| null |
https://arxiv.org/abs/2210.14666v2
|
https://arxiv.org/pdf/2210.14666v2.pdf
|
Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network
|
XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices. However, the mel-spectrogram generated by it is over-smoothing in middle- and high-frequency areas due to no special design for modeling the details of these parts. In this paper, we propose XiaoiceSing2, which can generate the details of middle- and high-frequency parts to better construct the full-band mel-spectrogram. Specifically, in order to alleviate this problem, XiaoiceSing2 adopts a generative adversarial network (GAN), which consists of a FastSpeech-based generator and a multi-band discriminator. We improve the feed-forward Transformer (FFT) block by adding multiple residual convolutional blocks in parallel with the self-attention block to balance the local and global features. The multi-band discriminator contains three sub-discriminators responsible for low-, middle-, and high-frequency parts of the mel-spectrogram, respectively. Each sub-discriminator is composed of several segment discriminators (SD) and detail discriminators (DD) to distinguish the audio from different aspects. The experiment on our internal 48kHz singing voice dataset shows XiaoiceSing2 significantly improves the quality of the singing voice over XiaoiceSing.
|
['Xing He', 'Chang Zeng', 'Chunhui Wang']
|
2022-10-26
| null | null | null | null |
['singing-voice-synthesis']
|
['speech']
|
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|
[15.483526229858398, 6.176003456115723]
|
115e3488-680c-45fb-9c05-b9655ef7eae1
|
reduction-of-class-activation-uncertainty
|
2305.03238
| null |
https://arxiv.org/abs/2305.03238v2
|
https://arxiv.org/pdf/2305.03238v2.pdf
|
Reduction of Class Activation Uncertainty with Background Information
|
Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieved improved generalization with much lower computation. We also investigate class activation mappings (CAMs) of the trained model and observed the tendency towards looking at a bigger picture in a few class classification problems with the proposed model training methodology. Applying transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on STL-10, CIFAR-10, CIFAR-100, Oxford-102, Caltech-101, and CINIC-10 datasets. Example scripts are available in the 'CAM' folder of the following GitHub Repository: github.com/dipuk0506/UQ
|
['H M Dipu Kabir']
|
2023-05-05
| null | null | null | null |
['fine-grained-image-classification']
|
['computer-vision']
|
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|
[9.395670890808105, 2.666675567626953]
|
3bbeb7ed-2343-4dc6-87a5-9a4a2891117c
|
womd-lidar-raw-sensor-dataset-benchmark-for
|
2304.03834
| null |
https://arxiv.org/abs/2304.03834v1
|
https://arxiv.org/pdf/2304.03834v1.pdf
|
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
|
Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems' predictions. Such intermediate representations tie the quality of the motion forecasting models to the performance of computer vision models. Moreover, the human-designed explicit interfaces between perception and motion forecasting typically pass only a subset of the semantic information present in the original sensory input. To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task. The new augmented dataset WOMD-LiDAR consists of over 100,000 scenes that each spans 20 seconds, consisting of well-synchronized and calibrated high quality LiDAR point clouds captured across a range of urban and suburban geographies (https://waymo.com/open/data/motion/). Compared to Waymo Open Dataset (WOD), WOMD-LiDAR dataset contains 100x more scenes. Furthermore, we integrate the LiDAR data into the motion forecasting model training and provide a strong baseline. Experiments show that the LiDAR data brings improvement in the motion forecasting task. We hope that WOMD-LiDAR will provide new opportunities for boosting end-to-end motion forecasting models.
|
['Dragomir Anguelov', 'Mingxing Tan', 'Weiyue Wang', 'Ivan Bogun', 'Mustafa Mustafa', 'Zhaoqi Leng', 'Pei Sun', 'Scott Ettinger', 'Zoey Yang', 'Xuanyu Zhou', 'Charles R. Qi', 'Rami Ai-Rfou', 'Hang Qiu', 'Runzhou Ge', 'Kan Chen']
|
2023-04-07
| null | null | null | null |
['motion-forecasting']
|
['computer-vision']
|
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|
[7.917141437530518, -2.0844171047210693]
|
8552245b-bcd0-4b4d-99d0-df415bcefb09
|
lf-pgvio-a-visual-inertial-odometry-framework
|
2306.06663
| null |
https://arxiv.org/abs/2306.06663v1
|
https://arxiv.org/pdf/2306.06663v1.pdf
|
LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments
|
In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. Notoriously, when the FoV of a panoramic camera reaches the negative half-plane, the image cannot be unfolded into a single pinhole image. Moreover, if a traditional straight-line detection method is directly applied to the original panoramic image, it cannot be normally used due to the large distortions in the panoramas and remains under-explored in the literature. To address these challenges, we put forward LF-PGVIO, which can provide line constraints for cameras with large FoV, even for cameras with negative-plane FoV, and directly extract omnidirectional curve segments from the raw omnidirectional image. We propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. Each point on the image is projected onto the sphere, and the detected omnidirectional curve segments in the image named geodesic segments must satisfy the criterion of being a geodesic segment on the unit sphere. The detected geodesic segment is sliced into multiple straight-line segments according to the radian of the geodesic, and descriptors are extracted separately and recombined to obtain new descriptors. Based on descriptor matching, we obtain the constraint relationship of the 3D line segments between multiple frames. In our VIO system, we use sliding window optimization using point feature residuals, line feature residuals, and IMU residuals. Our evaluation of the proposed system on public datasets demonstrates that LF-PGVIO outperforms state-of-the-art methods in terms of accuracy and robustness. Code will be open-sourced at https://github.com/flysoaryun/LF-PGVIO.
|
['Kaiwei Wang', 'Fei Gao', 'Yufan Zhang', 'Hao Shi', 'Kailun Yang', 'Ze Wang']
|
2023-06-11
| null | null | null | null |
['line-detection']
|
['computer-vision']
|
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|
[7.995668411254883, -2.132784366607666]
|
2dc6aaff-f769-4443-b6a0-a4cbe0a30412
|
adapted-human-pose-monocular-3d-human-pose
|
2105.10837
| null |
https://arxiv.org/abs/2105.10837v2
|
https://arxiv.org/pdf/2105.10837v2.pdf
|
Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data
|
The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect model performance. This issue is especially critical for the monocular 3D human pose estimation problem, in which 3D human data is often collected in a controlled lab setting. In this paper, we focus on alleviating the negative effect of domain shift in both appearance and pose space for 3D human pose estimation by presenting our adapted human pose (AHuP) approach. AHuP is built upon two key components: (1) semantically aware adaptation (SAA) for the cross-domain feature space adaptation, and (2) skeletal pose adaptation (SPA) for the pose space adaptation which takes only limited information from the target domain. By using zero real 3D human pose data, one of our adapted synthetic models shows comparable performance with the SOTA pose estimation models trained with large scale real 3D human datasets. The proposed SPA can be also employed independently as a light-weighted head to improve existing SOTA models in a novel context. A new 3D scan-based synthetic human dataset called ScanAva+ is also going to be publicly released with this work.
|
['Sarah Ostadabbas', 'Naveen Sehgal', 'Shuangjun Liu']
|
2021-05-23
| null | null | null | null |
['3d-pose-estimation', 'monocular-3d-human-pose-estimation']
|
['computer-vision', 'computer-vision']
|
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|
[7.022703170776367, -1.0529848337173462]
|
0c3b7d13-77b5-4552-8c19-a2297585e424
|
deep-learning-eliminates-massive-dust-storms
|
2206.10145
| null |
https://arxiv.org/abs/2206.10145v1
|
https://arxiv.org/pdf/2206.10145v1.pdf
|
Deep Learning Eliminates Massive Dust Storms from Images of Tianwen-1
|
Dust storms may remarkably degrade the imaging quality of Martian orbiters and delay the progress of mapping the global topography and geomorphology. To address this issue, this paper presents an approach that reuses the image dehazing knowledge obtained on Earth to resolve the dust-removal problem on Mars. In this approach, we collect remote-sensing images captured by Tianwen-1 and manually select hundreds of clean and dusty images. Inspired by the haze formation process on Earth, we formulate a similar visual degradation process on clean images and synthesize dusty images sharing a similar feature distribution with realistic dusty images. These realistic clean and synthetic dusty image pairs are used to train a deep model that inherently encodes dust irrelevant features and decodes them into dust-free images. Qualitative and quantitative results show that dust storms can be effectively eliminated by the proposed approach, leading to obviously improved topographical and geomorphological details of Mars.
|
['Long Xu', 'Xin Ren', 'Jia Li', 'Hongyu Li']
|
2022-06-21
| null | null | null | null |
['image-dehazing']
|
['computer-vision']
|
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|
[10.927091598510742, -3.2246201038360596]
|
29489fdc-19fd-48b5-8ffd-8cd3e4013663
|
synthesizing-coherent-story-with-auto
|
2211.1095
| null |
https://arxiv.org/abs/2211.10950v1
|
https://arxiv.org/pdf/2211.10950v1.pdf
|
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
|
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks and propose AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images. Moreover, AR-LDM can generalize to new characters through adaptation. To our best knowledge, this is the first work successfully leveraging diffusion models for coherent visual story synthesizing. Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the newly introduced challenging dataset VIST containing natural images. Large-scale human evaluations show that AR-LDM has superior performance in terms of quality, relevance, and consistency.
|
['Wenhu Chen', 'Hui Xue', 'Yuhong Li', 'Pengda Qin', 'Xichen Pan']
|
2022-11-20
| null | null | null | null |
['story-continuation', 'story-visualization']
|
['computer-vision', 'computer-vision']
|
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|
[11.157913208007812, 0.4077697694301605]
|
8b4552b1-f8fa-4ff8-b26d-47c9fd58bec3
|
learning-to-detect-instance-level-salient
|
2111.10137
| null |
https://arxiv.org/abs/2111.10137v1
|
https://arxiv.org/pdf/2111.10137v1.pdf
|
Learning to Detect Instance-level Salient Objects Using Complementary Image Labels
|
Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been considered in general saliency detection, it is mainly based on using class labels for object localization. However, it is non-trivial to use only class labels to learn instance-aware saliency information, as salient instances with high semantic affinities may not be easily separated by the labels. As the subitizing information provides an instant judgement on the number of salient items, it is naturally related to detecting salient instances and may help separate instances of the same class while grouping different parts of the same instance. Inspired by this observation, we propose to use class and subitizing labels as weak supervision for the SID problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids. This complementary information is then fused to produce a salient instance map. To facilitate the learning process, we further propose a progressive training scheme to reduce label noise and the corresponding noise learned by the model, via reciprocating the model with progressive salient instance prediction and model refreshing. Our extensive evaluations show that the proposed method plays favorably against carefully designed baseline methods adapted from related tasks.
|
['Rynson W. H. Lau', 'BaoCai Yin', 'Xin Yang', 'Ke Xu', 'Xin Tian']
|
2021-11-19
| null | null | null | null |
['boundary-detection']
|
['computer-vision']
|
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|
[9.808335304260254, -0.10521429032087326]
|
992c3ee5-a37d-40eb-906b-796394ab8fdc
|
joint-iris-segmentation-and-localization
|
1901.11195
| null |
https://arxiv.org/abs/1901.11195v2
|
https://arxiv.org/pdf/1901.11195v2.pdf
|
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
|
Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from poor performance when confronted with iris images captured in these conditions. Recent studies have shown that deep learning methods could achieve impressive performance on iris segmentation task. In addition, as iris is defined as an annular region between pupil and sclera, geometric constraints could be imposed to help locating the iris more accurately and improve the segmentation results. In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model. In particular, IrisParseNet firstly applies a Fully Convolutional Encoder-Decoder Attention Network to simultaneously estimate pupil center, iris segmentation mask and iris inner/outer boundary. Then, an effective post-processing method is adopted for iris inner/outer circle localization.To train and evaluate the proposed method, we manually label three challenging iris datasets, namely CASIA-Iris-Distance, UBIRIS.v2, and MICHE-I, which cover various types of noises. Extensive experiments are conducted on these newly annotated datasets, and results show that our method outperforms state-of-the-art methods on various benchmarks. All the ground-truth annotations, annotation codes and evaluation protocols are publicly available at https://github.com/xiamenwcy/IrisParseNet.
|
['Caiyong Wang', 'Yuhao Zhu', 'Zhenan Sun', 'Yunfan Liu', 'Ran He']
|
2019-01-31
| null | null | null | null |
['iris-segmentation']
|
['medical']
|
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|
[3.771373987197876, -3.6143786907196045]
|
8a5eccd2-922f-4d74-b3a8-d477a7210e3b
|
clustering-based-feature-learning-on-variable
|
1602.08977
| null |
http://arxiv.org/abs/1602.08977v1
|
http://arxiv.org/pdf/1602.08977v1.pdf
|
Clustering Based Feature Learning on Variable Stars
|
The success of automatic classification of variable stars strongly depends on
the lightcurve representation. Usually, lightcurves are represented as a vector
of many statistical descriptors designed by astronomers called features. These
descriptors commonly demand significant computational power to calculate,
require substantial research effort to develop and do not guarantee good
performance on the final classification task. Today, lightcurve representation
is not entirely automatic; algorithms that extract lightcurve features are
designed by humans and must be manually tuned up for every survey. The vast
amounts of data that will be generated in future surveys like LSST mean
astronomers must develop analysis pipelines that are both scalable and
automated. Recently, substantial efforts have been made in the machine learning
community to develop methods that prescind from expert-designed and manually
tuned features for features that are automatically learned from data. In this
work we present what is, to our knowledge, the first unsupervised feature
learning algorithm designed for variable stars. Our method first extracts a
large number of lightcurve subsequences from a given set of photometric data,
which are then clustered to find common local patterns in the time series.
Representatives of these patterns, called exemplars, are then used to transform
lightcurves of a labeled set into a new representation that can then be used to
train an automatic classifier. The proposed algorithm learns the features from
both labeled and unlabeled lightcurves, overcoming the bias generated when the
learning process is done only with labeled data. We test our method on MACHO
and OGLE datasets; the results show that the classification performance we
achieve is as good and in some cases better than the performance achieved using
traditional features, while the computational cost is significantly lower.
|
['Cristóbal Mackenzie', 'Karim Pichara', 'Pavlos Protopapas']
|
2016-02-29
| null | null | null | null |
['classification-of-variable-stars']
|
['miscellaneous']
|
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|
[7.747166156768799, 3.136155843734741]
|
a398e9e8-01a6-4028-8311-96043447b4e4
|
learning-guided-convolutional-network-for
|
1908.01238
| null |
https://arxiv.org/abs/1908.01238v1
|
https://arxiv.org/pdf/1908.01238v1.pdf
|
Learning Guided Convolutional Network for Depth Completion
|
Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion. Many neural networks have been designed for this task. However, they often na\"{\i}vely fuse the LiDAR data and RGB image information by performing feature concatenation or element-wise addition. Inspired by the guided image filtering, we design a novel guided network to predict kernel weights from the guidance image. These predicted kernels are then applied to extract the depth image features. In this way, our network generates content-dependent and spatially-variant kernels for multi-modal feature fusion. Dynamically generated spatially-variant kernels could lead to prohibitive GPU memory consumption and computation overhead. We further design a convolution factorization to reduce computation and memory consumption. The GPU memory reduction makes it possible for feature fusion to work in multi-stage scheme. We conduct comprehensive experiments to verify our method on real-world outdoor, indoor and synthetic datasets. Our method produces strong results. It outperforms state-of-the-art methods on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. It also presents strong generalization capability under different 3D point densities, various lighting and weather conditions as well as cross-dataset evaluations. The code will be released for reproduction.
|
['Fei-Peng Tian', 'Ping Tan', 'Jie Tang', 'Jian Li', 'Wei Feng']
|
2019-08-03
| null | null | null | null |
['stereo-lidar-fusion']
|
['computer-vision']
|
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|
[8.498393058776855, -2.3971409797668457]
|
7b7f14c1-783a-4718-955f-1bee7e684984
|
on-the-use-of-higher-order-tensors-to-model
|
2007.01949
| null |
https://arxiv.org/abs/2007.01949v1
|
https://arxiv.org/pdf/2007.01949v1.pdf
|
On the use of higher-order tensors to model muscle synergies
|
The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing multi-channel surface Electromyography (EMG) signals into a synergy matrix (spatial mode) and its weighting function (temporal mode). This is done using several matrix factorisation techniques, with Non-negative matrix factorisation (NMF) being the most prominent method. Here, we introduce a 4th-order tensor muscle synergy model that extends the current state of the art by taking spectral information and repetitions (movements) into account. This adds more depth to the model and provides more synergistic information. In particular, we illustrate a proof-of-concept study where the Tucker3 tensor decomposition model was applied to a subset of wrist movements from the Ninapro database. The results showed the potential of Tucker3 tensor factorisation in finding patterns of muscle synergies with information about the movements and highlights the differences between the current and proposed model.
|
['Javier Escudero', 'Eli Kinney-Lang', 'Loukianos Spyrou', 'Ahmed Ebied']
|
2020-07-03
| null | null | null | null |
['electromyography-emg']
|
['medical']
|
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|
[6.877106189727783, 0.2097569704055786]
|
eb8507db-58c9-43a6-bf9c-9df8569cc96a
|
a-3d-coarse-to-fine-framework-for-volumetric
|
1712.00201
| null |
http://arxiv.org/abs/1712.00201v2
|
http://arxiv.org/pdf/1712.00201v2.pdf
|
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
|
In this paper, we adopt 3D Convolutional Neural Networks to segment
volumetric medical images. Although deep neural networks have been proven to be
very effective on many 2D vision tasks, it is still challenging to apply them
to 3D tasks due to the limited amount of annotated 3D data and limited
computational resources. We propose a novel 3D-based coarse-to-fine framework
to effectively and efficiently tackle these challenges. The proposed 3D-based
framework outperforms the 2D counterpart to a large margin since it can
leverage the rich spatial infor- mation along all three axes. We conduct
experiments on two datasets which include healthy and pathological pancreases
respectively, and achieve the current state-of-the-art in terms of
Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset,
we outperform the previous best by an average of over 2%, and the worst case is
improved by 7% to reach almost 70%, which indicates the reliability of our
framework in clinical applications.
|
['Yingda Xia', 'Elliot K. Fishman', 'Wei Shen', 'Zhuotun Zhu', 'Alan L. Yuille']
|
2017-12-01
| null | null | null | null |
['volumetric-medical-image-segmentation', 'pancreas-segmentation']
|
['medical', 'medical']
|
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|
[14.503011703491211, -2.560307264328003]
|
1705944f-a8a5-4f02-b9d1-ec50e92e4d01
|
understanding-cyber-athletes-behaviour
|
1908.06407
| null |
https://arxiv.org/abs/1908.06407v1
|
https://arxiv.org/pdf/1908.06407v1.pdf
|
Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
|
eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: `cyber athletes' (Monolith team), semi-professional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis. Our results demonstrate that the professional athletes can be identified by their behaviour on the chair while playing the game.
|
['Rostislav Shaniiazov', 'Andrey Somov', 'Anastasia Kiskun', 'Evgeny Burnaev', 'Anton Smerdov']
|
2019-08-18
| null | null | null | null |
['sensor-modeling', 'skills-evaluation', 'skills-assessment', 'fps-games']
|
['computer-vision', 'computer-vision', 'computer-vision', 'playing-games']
|
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|
[6.942926406860352, 0.3482387363910675]
|
2bf49385-0753-40ae-aeb9-68aa1da8cfee
|
bridging-the-modality-gap-for-speech-to-text
|
2010.1492
| null |
https://arxiv.org/abs/2010.14920v1
|
https://arxiv.org/pdf/2010.14920v1.pdf
|
Bridging the Modality Gap for Speech-to-Text Translation
|
End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously, which ignores the speech-and-text modality differences and makes the encoder overloaded, leading to great difficulty in learning such a model. To address these issues, we propose a Speech-to-Text Adaptation for Speech Translation (STAST) model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text. Specifically, we decouple the speech translation encoder into three parts and introduce a shrink mechanism to match the length of speech representation with that of the corresponding text transcription. To obtain better semantic representation, we completely integrate a text-based translation model into the STAST so that two tasks can be trained in the same latent space. Furthermore, we introduce a cross-modal adaptation method to close the distance between speech and text representation. Experimental results on English-French and English-German speech translation corpora have shown that our model significantly outperforms strong baselines, and achieves the new state-of-the-art performance.
|
['Chengqing Zong', 'Jiajun Zhang', 'Junnan Zhu', 'Yuchen Liu']
|
2020-10-28
| null | null | null | null |
['speech-to-text-translation']
|
['natural-language-processing']
|
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|
[14.514341354370117, 7.197293281555176]
|
c9f3599a-5034-4a23-b660-ac9e2d8ccdcf
|
self-knowledge-distillation-for-surgical
|
2306.08961
| null |
https://arxiv.org/abs/2306.08961v1
|
https://arxiv.org/pdf/2306.08961v1.pdf
|
Self-Knowledge Distillation for Surgical Phase Recognition
|
Purpose: Advances in surgical phase recognition are generally led by training deeper networks. Rather than going further with a more complex solution, we believe that current models can be exploited better. We propose a self-knowledge distillation framework that can be integrated into current state-of-the-art (SOTA) models without requiring any extra complexity to the models or annotations. Methods: Knowledge distillation is a framework for network regularization where knowledge is distilled from a teacher network to a student network. In self-knowledge distillation, the student model becomes the teacher such that the network learns from itself. Most phase recognition models follow an encoder-decoder framework. Our framework utilizes self-knowledge distillation in both stages. The teacher model guides the training process of the student model to extract enhanced feature representations from the encoder and build a more robust temporal decoder to tackle the over-segmentation problem. Results: We validate our proposed framework on the public dataset Cholec80. Our framework is embedded on top of four popular SOTA approaches and consistently improves their performance. Specifically, our best GRU model boosts performance by +3.33% accuracy and +3.95% F1-score over the same baseline model. Conclusion: We embed a self-knowledge distillation framework for the first time in the surgical phase recognition training pipeline. Experimental results demonstrate that our simple yet powerful framework can improve performance of existing phase recognition models. Moreover, our extensive experiments show that even with 75% of the training set we still achieve performance on par with the same baseline model trained on the full set.
|
['Imanol Luengo', 'Danail Stoyanov', 'Abdolrahim Kadkhodamohammadi', 'Santiago Barbarisi', 'Jinglu Zhang']
|
2023-06-15
| null | null | null | null |
['self-knowledge-distillation', 'surgical-phase-recognition']
|
['computer-vision', 'computer-vision']
|
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|
[14.160638809204102, -3.2707152366638184]
|
f4092fa4-55d5-4444-b618-915178ef703d
|
detection-of-poisoning-attacks-with-anomaly
|
2207.08486
| null |
https://arxiv.org/abs/2207.08486v2
|
https://arxiv.org/pdf/2207.08486v2.pdf
|
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
|
Adversarial attacks such as poisoning attacks have attracted the attention of many machine learning researchers. Traditionally, poisoning attacks attempt to inject adversarial training data in order to manipulate the trained model. In federated learning (FL), data poisoning attacks can be generalized to model poisoning attacks, which cannot be detected by simpler methods due to the lack of access to local training data by the detector. State-of-the-art poisoning attack detection methods for FL have various weaknesses, e.g., the number of attackers has to be known or not high enough, working with i.i.d. data only, and high computational complexity. To overcome above weaknesses, we propose a novel framework for detecting poisoning attacks in FL, which employs a reference model based on a public dataset and an auditor model to detect malicious updates. We implemented a detector based on the proposed framework and using a one-class support vector machine (OC-SVM), which reaches the lowest possible computational complexity O(K) where K is the number of clients. We evaluated our detector's performance against state-of-the-art (SOTA) poisoning attacks for two typical applications of FL: electrocardiograph (ECG) classification and human activity recognition (HAR). Our experimental results validated the performance of our detector over other SOTA detection methods.
|
['Ludovic Koehl', 'Kim-Phuc Tran', 'Shujun Li', 'Ali Raza']
|
2022-07-18
| null | null | null | null |
['data-poisoning', 'ecg-classification']
|
['adversarial', 'medical']
|
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|
[5.633913516998291, 7.098053455352783]
|
48228857-2b6e-476d-922a-79c417ce3366
|
cflownets-continuous-control-with-generative
|
2303.0243
| null |
https://arxiv.org/abs/2303.02430v1
|
https://arxiv.org/pdf/2303.02430v1.pdf
|
CFlowNets: Continuous Control with Generative Flow Networks
|
Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNet aims to generate distribution proportional to the rewards over terminating states, and to sample different candidates in an active learning fashion. GFlowNets need to form a DAG and compute the flow matching loss by traversing the inflows and outflows of each node in the trajectory. No experiments have yet concluded that GFlowNets can be used to handle continuous tasks. In this paper, we propose generative continuous flow networks (CFlowNets) that can be applied to continuous control tasks. First, we present the theoretical formulation of CFlowNets. Then, a training framework for CFlowNets is proposed, including the action selection process, the flow approximation algorithm, and the continuous flow matching loss function. Afterward, we theoretically prove the error bound of the flow approximation. The error decreases rapidly as the number of flow samples increases. Finally, experimental results on continuous control tasks demonstrate the performance advantages of CFlowNets compared to many reinforcement learning methods, especially regarding exploration ability.
|
['Jianye Hao', 'Haozhi Wang', 'Shuang Luo', 'Yinchuan Li']
|
2023-03-04
| null | null | null | null |
['continuous-control']
|
['playing-games']
|
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2.97874361e-01 -8.80647123e-01 -3.42006892e-01 -4.29463983e-01]
|
[4.037796497344971, 2.207622766494751]
|
a07b7767-e066-4181-a61d-8385e0319bfc
|
deep-feature-synthesis-towards-automating
| null | null |
https://ieeexplore.ieee.org/abstract/document/7344858
|
http://www.jmaxkanter.com/static/papers/DSAA_DSM_2015.pdf
|
Deep Feature Synthesis: Towards Automating Data Science Endeavors
|
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically. To achieve this automation, we first propose and develop the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. The algorithm follows relationships in the data to a base field, and then sequentially applies mathematical functions along that path to create the final feature. Second, we implement a generalizable machine learning pipeline and tune it using a novel Gaussian Copula process based approach. We entered the Data Science Machine in 3 data science competitions that featured 906 other data science teams. Our approach beats 615 teams in these data science competitions. In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieved 94% of the best competitor's score. In the best case, with an ongoing competition, we beat 85.6% of the teams and achieved 95.7% of the top submissions score.
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['Kalyan Veeramachaneni', 'James Max Kanter']
|
2015-01-01
| null | null | null |
dsaa-2015-2015-1
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['automated-feature-engineering']
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['methodology']
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|
[8.912678718566895, 7.27034854888916]
|
68ccb47f-84d8-490d-afb1-aa556f260a2d
|
large-scale-fine-grained-categorization-and
|
1806.06193
| null |
http://arxiv.org/abs/1806.06193v1
|
http://arxiv.org/pdf/1806.06193v1.pdf
|
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
|
Transferring the knowledge learned from large scale datasets (e.g., ImageNet)
via fine-tuning offers an effective solution for domain-specific fine-grained
visual categorization (FGVC) tasks (e.g., recognizing bird species or car make
and model). In such scenarios, data annotation often calls for specialized
domain knowledge and thus is difficult to scale. In this work, we first tackle
a problem in large scale FGVC. Our method won first place in iNaturalist 2017
large scale species classification challenge. Central to the success of our
approach is a training scheme that uses higher image resolution and deals with
the long-tailed distribution of training data. Next, we study transfer learning
via fine-tuning from large scale datasets to small scale, domain-specific FGVC
datasets. We propose a measure to estimate domain similarity via Earth Mover's
Distance and demonstrate that transfer learning benefits from pre-training on a
source domain that is similar to the target domain by this measure. Our
proposed transfer learning outperforms ImageNet pre-training and obtains
state-of-the-art results on multiple commonly used FGVC datasets.
|
['Yang song', 'Serge Belongie', 'Chen Sun', 'Andrew Howard', 'Yin Cui']
|
2018-06-16
|
large-scale-fine-grained-categorization-and-1
|
http://openaccess.thecvf.com/content_cvpr_2018/html/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Cui_Large_Scale_Fine-Grained_CVPR_2018_paper.pdf
|
cvpr-2018-6
|
['fine-grained-visual-categorization']
|
['computer-vision']
|
[ 1.55280652e-02 -5.20442545e-01 -2.60445714e-01 -4.03274000e-01
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|
[9.847728729248047, 2.2779839038848877]
|
295602ae-74f5-4155-9f05-3acdc2ccf305
|
supervising-unsupervised-open-information
| null | null |
https://aclanthology.org/D19-1067
|
https://aclanthology.org/D19-1067.pdf
|
Supervising Unsupervised Open Information Extraction Models
|
We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance. It uses the outputs of multiple unsupervised Open IE systems plus a diverse set of lexical and syntactic information such as word embedding, part-of-speech embedding, syntactic role embedding and dependency structure as its input features and produces a sequence of word labels indicating whether the word belongs to a relation, the arguments of the relation or irrelevant. Comparing with existing supervised Open IE systems, our approach leverages the knowledge in existing unsupervised Open IE systems to overcome the problem of insufficient training data. By employing multiple unsupervised Open IE systems, our system learns to combine the strength and avoid the weakness in each individual Open IE system. We have conducted experiments on multiple labeled benchmark data sets. Our evaluation results have demonstrated the superiority of the proposed method over existing supervised and unsupervised models by a significant margin.
|
['SHimei Pan', 'Taesung Lee', 'Youngja Park', 'Arpita Roy']
|
2019-11-01
| null | null | null |
ijcnlp-2019-11
|
['role-embedding', 'open-information-extraction']
|
['graphs', 'natural-language-processing']
|
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|
[9.424266815185547, 8.61453914642334]
|
a2095ce6-35d9-4dd1-aba6-9992c3eb6660
|
investigating-sindy-as-a-tool-for-causal
|
2212.14133
| null |
https://arxiv.org/abs/2212.14133v1
|
https://arxiv.org/pdf/2212.14133v1.pdf
|
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals
|
The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.
|
['Edward Kim', 'Rosina Weber', "Andrew O'Brien"]
|
2022-12-29
| null | null | null | null |
['causal-discovery']
|
['knowledge-base']
|
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|
[7.696506023406982, 5.184157371520996]
|
4e18c08c-7a10-434a-8a04-6e2c315c824e
|
benchmarking-the-performance-of-bayesian
|
2106.01309
| null |
https://arxiv.org/abs/2106.01309v1
|
https://arxiv.org/pdf/2106.01309v1.pdf
|
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
|
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.
|
['Tonio Buonassisi', 'John Fisher III', 'Keith A. Brown', 'Benji Maruyama', 'Kedar Hippalgaonkar', 'Saif A. Khan', 'Flore Mekki-Berrada', 'Daniil Bash', 'James R. Deneault', 'Shijing Sun', 'Zhe Liu', 'Armi Tiihonen', 'Zekun Ren', 'Aldair E. Gongora', 'Qiaohao Liang']
|
2021-05-23
| null | null | null | null |
['bayesian-optimisation']
|
['methodology']
|
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|
[5.909114360809326, 4.261407852172852]
|
b6c22d24-96b5-48eb-8b19-5b53e929fc75
|
theoretical-limitations-of-self-attention-in
|
1906.06755
| null |
https://arxiv.org/abs/1906.06755v2
|
https://arxiv.org/pdf/1906.06755v2.pdf
|
Theoretical Limitations of Self-Attention in Neural Sequence Models
|
Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention. Previous work has suggested that the computational capabilities of self-attention to process hierarchical structures are limited. In this work, we mathematically investigate the computational power of self-attention to model formal languages. Across both soft and hard attention, we show strong theoretical limitations of the computational abilities of self-attention, finding that it cannot model periodic finite-state languages, nor hierarchical structure, unless the number of layers or heads increases with input length. These limitations seem surprising given the practical success of self-attention and the prominent role assigned to hierarchical structure in linguistics, suggesting that natural language can be approximated well with models that are too weak for the formal languages typically assumed in theoretical linguistics.
|
['Michael Hahn']
|
2019-06-16
|
theoretical-limitations-of-self-attention-in-1
|
https://aclanthology.org/2020.tacl-1.11
|
https://aclanthology.org/2020.tacl-1.11.pdf
|
tacl-2020-1
|
['hard-attention']
|
['methodology']
|
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|
[10.610673904418945, 9.049579620361328]
|
8f4c50e6-ba42-46aa-8e99-46a489682820
|
dexray-a-simple-yet-effective-deep-learning
|
2109.03326
| null |
https://arxiv.org/abs/2109.03326v1
|
https://arxiv.org/pdf/2109.03326v1.pdf
|
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
|
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attractive to other fields such as malware detection, where deep learning on images alleviates the need for comprehensively hand-crafted features generalising to different malware variants. We postulate that this research direction could become the next frontier in Android malware detection, and therefore requires a clear roadmap to ensure that new approaches indeed bring novel contributions. We contribute with a first building block by developing and assessing a baseline pipeline for image-based malware detection with straightforward steps. We propose DexRay, which converts the bytecode of the app DEX files into grey-scale "vector" images and feeds them to a 1-dimensional Convolutional Neural Network model. We view DexRay as foundational due to the exceedingly basic nature of the design choices, allowing to infer what could be a minimal performance that can be obtained with image-based learning in malware detection. The performance of DexRay evaluated on over 158k apps demonstrates that, while simple, our approach is effective with a high detection rate(F1-score= 0.96). Finally, we investigate the impact of time decay and image-resizing on the performance of DexRay and assess its resilience to obfuscation. This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.
|
['Jacques Klein', 'Tegawendé F. Bissyandé', 'Kevin Allix', 'Abdoul Kader Kabore', 'Jordan Samhi', 'Nadia Daoudi']
|
2021-09-05
| null | null | null | null |
['android-malware-detection']
|
['miscellaneous']
|
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|
[14.42869758605957, 9.682647705078125]
|
9b4e3404-9b17-4b4f-bb50-c7756f233a56
|
self-supervised-approach-for-facial-movement
|
2105.01256
| null |
https://arxiv.org/abs/2105.01256v1
|
https://arxiv.org/pdf/2105.01256v1.pdf
|
Self-Supervised Approach for Facial Movement Based Optical Flow
|
Computing optical flow is a fundamental problem in computer vision. However, deep learning-based optical flow techniques do not perform well for non-rigid movements such as those found in faces, primarily due to lack of the training data representing the fine facial motion. We hypothesize that learning optical flow on face motion data will improve the quality of predicted flow on faces. The aim of this work is threefold: (1) exploring self-supervised techniques to generate optical flow ground truth for face images; (2) computing baseline results on the effects of using face data to train Convolutional Neural Networks (CNN) for predicting optical flow; and (3) using the learned optical flow in micro-expression recognition to demonstrate its effectiveness. We generate optical flow ground truth using facial key-points in the BP4D-Spontaneous dataset. The generated optical flow is used to train the FlowNetS architecture to test its performance on the generated dataset. The performance of FlowNetS trained on face images surpassed that of other optical flow CNN architectures, demonstrating its usefulness. Our optical flow features are further compared with other methods using the STSTNet micro-expression classifier, and the results indicate that the optical flow obtained using this work has promising applications in facial expression analysis.
|
['Abhinav Dhall', 'Usman Tariq', 'Muhannad Alkaddour']
|
2021-05-04
| null | null | null | null |
['micro-expression-recognition']
|
['computer-vision']
|
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|
[13.627005577087402, 1.6436467170715332]
|
f4dc2885-c8cb-4a9a-8181-45ad36ebdbba
|
autofi-towards-automatic-wifi-human-sensing
|
2205.01629
| null |
https://arxiv.org/abs/2205.01629v2
|
https://arxiv.org/pdf/2205.01629v2.pdf
|
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning
|
WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly-captured CSI samples can be easily collected. {\color{black}In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient WiFi sensing model based on a novel geometric self-supervised learning algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement.
|
['Lihua Xie', 'Dazhuo Wang', 'Han Zou', 'Xinyan Chen', 'Jianfei Yang']
|
2022-04-12
| null | null | null | null |
['gait-recognition', 'gesture-recognition']
|
['computer-vision', 'computer-vision']
|
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|
[6.72189998626709, 0.7008734345436096]
|
da4d73a5-68d0-49ca-9230-9e7a160512cc
|
language-conditioned-imitation-learning-with
|
2305.19075
| null |
https://arxiv.org/abs/2305.19075v2
|
https://arxiv.org/pdf/2305.19075v2.pdf
|
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
|
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environments, they encounter limitations in adapting to unfamiliar environment settings. In this study, we propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data to enhance the algorithm's generalization in adapting to unfamiliar environments. We assess our model's performance in both simulated and real-world environments using a zero-shot setting. In the simulated environment, the proposed approach surpasses previously reported scores for CALVIN benchmark, especially in the challenging Zero-Shot Multi-Environment setting. The average completed task length, indicating the average number of tasks the agent can continuously complete, improves more than 2.5 times compared to the state-of-the-art method HULC. In addition, we conduct a zero-shot evaluation of our policy in a real-world setting, following training exclusively in simulated environments without additional specific adaptations. In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world. For further details, including access to our code and videos, please refer to https://demoviewsite.wixsite.com/spil
|
['Alois Knoll', 'Kai Huang', 'Chenguang Yang', 'Xiaojie Su', 'Xiangtong Yao', 'Zhenshan Bing', 'Hongkuan Zhou']
|
2023-05-30
| null | null | null | null |
['robot-manipulation']
|
['robots']
|
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|
[4.4876203536987305, 0.9633731842041016]
|
e925763f-b213-40b6-8a9d-b447035a368b
|
race-bias-analysis-of-bona-fide-errors-in
|
2210.05366
| null |
https://arxiv.org/abs/2210.05366v1
|
https://arxiv.org/pdf/2210.05366v1.pdf
|
Race Bias Analysis of Bona Fide Errors in face anti-spoofing
|
The study of bias in Machine Learning is receiving a lot of attention in recent years, however, few only papers deal explicitly with the problem of race bias in face anti-spoofing. In this paper, we present a systematic study of race bias in face anti-spoofing with three key characteristics: the focus is on analysing potential bias in the bona fide errors, where significant ethical and legal issues lie; the analysis is not restricted to the final binary outcomes of the classifier, but also covers the classifier's scalar responses and its latent space; the threshold determining the operating point of the classifier is considered a variable. We demonstrate the proposed bias analysis process on a VQ-VAE based face anti-spoofing algorithm, trained on the Replay Attack and the Spoof in the Wild (SiW) databases, and analysed for bias on the SiW and Racial Faces in the Wild (RFW), databases. The results demonstrate that race bias is not necessarily the result of different mean response values among the various populations. Instead, it can be better understood as the combined effect of several possible characteristics of the response distributions: different means; different variances; bimodal behaviour; existence of outliers.
|
['Ioannis Ivrissimtzis', 'Latifah Abduh']
|
2022-10-11
| null | null | null | null |
['face-anti-spoofing']
|
['computer-vision']
|
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|
[13.010695457458496, 1.2600202560424805]
|
7fb1dd6f-b960-4a7e-9959-7bdbfeb405bc
|
cross-task-knowledge-transfer-for-query-based
| null | null |
https://aclanthology.org/D19-5810
|
https://aclanthology.org/D19-5810.pdf
|
Cross-Task Knowledge Transfer for Query-Based Text Summarization
|
We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization. Using an MRC model trained on the SQuAD1.1 dataset as a core system component, we first build an extractive query-based summarizer. For better precision, this summarizer also compresses the output of the MRC model using a novel sentence compression technique. We further leverage pre-trained machine translation systems to abstract our extracted summaries. Our models achieve state-of-the-art results on the publicly available CNN/Daily Mail and Debatepedia datasets, and can serve as simple yet powerful baselines for future systems. We also hope that these results will encourage research on transfer learning from large MRC corpora to query-based summarization.
|
['Md. Arafat Sultan', 'Elozino Egonmwan', 'Vittorio Castelli']
|
2019-11-01
| null | null | null |
ws-2019-11
|
['sentence-compression']
|
['natural-language-processing']
|
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|
[12.22586727142334, 9.276823043823242]
|
a2474f3a-c654-4cc5-9ede-25638de15b7e
|
grim-a-general-real-time-deep-learning
|
2108.11033
| null |
https://arxiv.org/abs/2108.11033v1
|
https://arxiv.org/pdf/2108.11033v1.pdf
|
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
|
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resource-constrained'' when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to 14.08x speedup.
|
['Bin Ren', 'Yanzhi Wang', 'Xue Lin', 'Xuehai Qian', 'Gang Zhou', 'Peiyan Dong', 'Xiaolong Ma', 'Zhengang Li', 'Wei Niu']
|
2021-08-25
| null | null | null | null |
['compiler-optimization']
|
['computer-code']
|
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|
[8.614895820617676, 3.1955068111419678]
|
8fe906b8-c76c-44e3-af66-0e02db8bf67d
|
feature-adversarial-distillation-for-point
|
2306.14221
| null |
https://arxiv.org/abs/2306.14221v2
|
https://arxiv.org/pdf/2306.14221v2.pdf
|
Feature Adversarial Distillation for Point Cloud Classification
|
Due to the point cloud's irregular and unordered geometry structure, conventional knowledge distillation technology lost a lot of information when directly used on point cloud tasks. In this paper, we propose Feature Adversarial Distillation (FAD) method, a generic adversarial loss function in point cloud distillation, to reduce loss during knowledge transfer. In the feature extraction stage, the features extracted by the teacher are used as the discriminator, and the students continuously generate new features in the training stage. The feature of the student is obtained by attacking the feedback from the teacher and getting a score to judge whether the student has learned the knowledge well or not. In experiments on standard point cloud classification on ModelNet40 and ScanObjectNN datasets, our method reduced the information loss of knowledge transfer in distillation in 40x model compression while maintaining competitive performance.
|
['Wei Wu', 'YuXing Lee']
|
2023-06-25
| null | null | null | null |
['point-cloud-classification', 'classification-1', 'model-compression', 'transfer-learning']
|
['computer-vision', 'methodology', 'methodology', 'miscellaneous']
|
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|
[7.794238567352295, -4.36268424987793]
|
639ebd02-85e7-41e4-ae03-90db4f7cb050
|
make-it-3d-high-fidelity-3d-creation-from-a
|
2303.14184
| null |
https://arxiv.org/abs/2303.14184v2
|
https://arxiv.org/pdf/2303.14184v2.pdf
|
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
|
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization pipeline: the first stage optimizes a neural radiance field by incorporating constraints from the reference image at the frontal view and diffusion prior at novel views; the second stage transforms the coarse model into textured point clouds and further elevates the realism with diffusion prior while leveraging the high-quality textures from the reference image. Extensive experiments demonstrate that our method outperforms prior works by a large margin, resulting in faithful reconstructions and impressive visual quality. Our method presents the first attempt to achieve high-quality 3D creation from a single image for general objects and enables various applications such as text-to-3D creation and texture editing.
|
['Dong Chen', 'Lizhuang Ma', 'Ran Yi', 'Ting Zhang', 'Bo Zhang', 'Tengfei Wang', 'Junshu Tang']
|
2023-03-24
| null | null | null | null |
['text-to-3d']
|
['computer-vision']
|
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|
[9.277917861938477, -3.1303722858428955]
|
a45753c5-2f83-4ae6-83e8-100d226dcda8
|
a-survey-on-machine-learning-techniques-for-1
|
2110.0961
| null |
https://arxiv.org/abs/2110.09610v2
|
https://arxiv.org/pdf/2110.09610v2.pdf
|
A Survey on Machine Learning Techniques for Source Code Analysis
|
The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 479 primary studies published between 2011 and 2021. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources.
|
['Federica Sarro', 'Hadi Moazen', 'Indira Vats', 'Rohit Tiwari', 'Stefanos Georgiou', 'Maria Kechagia', 'Tushar Sharma']
|
2021-10-18
| null | null | null | null |
['vulnerability-detection']
|
['miscellaneous']
|
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-3.22500855e-01 1.16004661e-01 8.27551603e-01 7.14332640e-01
7.06148922e-01 6.66581571e-01 4.14784908e-01 -4.42103565e-01
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|
[7.343923568725586, 7.7478861808776855]
|
3e303a4a-c31c-42bd-b57e-96a4328728fd
|
learning-temporal-consistency-for-source-free
|
2203.04559
| null |
https://arxiv.org/abs/2203.04559v4
|
https://arxiv.org/pdf/2203.04559v4.pdf
|
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
|
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments. However, these methods require constant access to source data during the adaptation process. Yet in many real-world applications, subjects and scenes in the source video domain should be irrelevant to those in the target video domain. With the increasing emphasis on data privacy, such methods that require source data access would raise serious privacy issues. Therefore, to cope with such concern, a more practical domain adaptation scenario is formulated as the Source-Free Video-based Domain Adaptation (SFVDA). Though there are a few methods for Source-Free Domain Adaptation (SFDA) on image data, these methods yield degenerating performance in SFVDA due to the multi-modality nature of videos, with the existence of additional temporal features. In this paper, we propose a novel Attentive Temporal Consistent Network (ATCoN) to address SFVDA by learning temporal consistency, guaranteed by two novel consistency objectives, namely feature consistency and source prediction consistency, performed across local temporal features. ATCoN further constructs effective overall temporal features by attending to local temporal features based on prediction confidence. Empirical results demonstrate the state-of-the-art performance of ATCoN across various cross-domain action recognition benchmarks.
|
['Zhenghua Chen', 'Wu Min', 'Keyu Wu', 'Haozhi Cao', 'Jianfei Yang', 'Yuecong Xu']
|
2022-03-09
| null | null | null | null |
['source-free-domain-adaptation']
|
['computer-vision']
|
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-1.54291108e-01 -3.89892012e-02 9.90517437e-02 9.66806948e-01
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|
[8.669146537780762, 0.8459590077400208]
|
ce2073b9-80b8-4e3f-8e19-cd1cf24e5ced
|
does-synthetic-data-generation-of-llms-help
|
2303.0436
| null |
https://arxiv.org/abs/2303.04360v2
|
https://arxiv.org/pdf/2303.04360v2.pdf
|
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
|
Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation. However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. However, our preliminary results indicate that employing ChatGPT directly for these tasks resulted in poor performance and raised privacy concerns associated with uploading patients' information to the ChatGPT API. To overcome these limitations, we propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data with labels utilizing ChatGPT and fine-tuning a local model for the downstream task. Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23.37% to 63.99% for the named entity recognition task and from 75.86% to 83.59% for the relation extraction task. Furthermore, generating data using ChatGPT can significantly reduce the time and effort required for data collection and labeling, as well as mitigate data privacy concerns. In summary, the proposed framework presents a promising solution to enhance the applicability of LLM models to clinical text mining.
|
['Xia Hu', 'Xiaoqian Jiang', 'Xiaotian Han', 'Ruixiang Tang']
|
2023-03-08
| null | null | null | null |
['synthetic-data-generation', 'synthetic-data-generation']
|
['medical', 'miscellaneous']
|
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|
[8.448080062866211, 8.673523902893066]
|
795d3392-987b-4823-b79b-b7b65d57e933
|
deceptive-opinion-spam-detection-using-neural
| null | null |
https://aclanthology.org/C16-1014
|
https://aclanthology.org/C16-1014.pdf
|
Deceptive Opinion Spam Detection Using Neural Network
|
Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. In particular, given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector. Finally, the document representation is used directly as features to identify deceptive opinion spam. Experimental results on three domains (Hotel, Restaurant, and Doctor) show that our proposed method outperforms state-of-the-art methods.
|
['Yue Zhang', 'Yafeng Ren']
|
2016-12-01
|
deceptive-opinion-spam-detection-using-neural-1
|
https://aclanthology.org/C16-1014
|
https://aclanthology.org/C16-1014.pdf
|
coling-2016-12
|
['spam-detection']
|
['natural-language-processing']
|
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|
[7.8923659324646, 10.012715339660645]
|
95cda3f3-f6cb-4543-93d8-55ba791726b2
|
liver-segmentation-using-turbolift-learning
|
2207.10167
| null |
https://arxiv.org/abs/2207.10167v2
|
https://arxiv.org/pdf/2207.10167v2.pdf
|
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
|
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874$\pm$0.031 and 0.905$\pm$0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
|
['Georg Rose', 'Andreas Nürnberger', 'Oliver Speck', 'Thomas Werncke', 'Inga Brüsch', 'Frank Wacker', 'Bennet Hensen', 'Vladimir Semshchikov', 'Vojtěch Kulvait', 'Robert Frysch', 'Soumick Chatterjee', 'Hana Haseljić']
|
2022-07-20
| null | null | null | null |
['liver-segmentation']
|
['medical']
|
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-4.70534056e-01 4.81263608e-01 8.32525909e-01 7.20793307e-01
-4.23405841e-02 8.42743218e-01 6.35706544e-01 -9.93342221e-01
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2.84006894e-01 -3.93481284e-01 -5.08577228e-01 1.15503073e-01]
|
[14.477995872497559, -2.706308126449585]
|
a09bd47b-3577-4c37-b034-9b949dd57ed7
|
nested-invariance-pooling-and-rbm-hashing-for
|
1603.04595
| null |
http://arxiv.org/abs/1603.04595v2
|
http://arxiv.org/pdf/1603.04595v2.pdf
|
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
|
The goal of this work is the computation of very compact binary hashes for
image instance retrieval. Our approach has two novel contributions. The first
one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a
mathematical theory for computing group invariant transformations with
feed-forward neural networks. NIP is able to produce compact and
well-performing descriptors with visual representations extracted from
convolutional neural networks. We specifically incorporate scale, translation
and rotation invariances but the scheme can be extended to any arbitrary sets
of transformations. We also show that using moments of increasing order
throughout nesting is important. The NIP descriptors are then hashed to the
target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel
batch-level regularization scheme specifically designed for the purpose of
hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows
that the results obtained both with the NIP descriptors and the NIP+RBMH hashes
are consistently outstanding across a wide range of datasets.
|
['Tomaso Poggio', 'Olivier Morère', 'Antoine Veillard', 'Jie Lin', 'Vijay Chandrasekhar']
|
2016-03-15
| null | null | null | null |
['image-instance-retrieval']
|
['computer-vision']
|
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-2.28297710e-01 -2.46583819e-02 8.35674644e-01 5.98592341e-01
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6.04574025e-01 -4.36178952e-01 -4.69888568e-01 -2.72310898e-02]
|
[10.640789985656738, 0.5181881189346313]
|
0e4164ab-2447-4058-8298-4919d01385e7
|
understanding-and-leveraging
| null | null |
https://openreview.net/forum?id=shbAgEsk3qM
|
https://openreview.net/pdf?id=shbAgEsk3qM
|
Understanding and Leveraging Overparameterization in Recursive Value Estimation
|
The theory of function approximation in reinforcement learning (RL) typically considers low capacity representations that incur a tradeoff between approximation error, stability and generalization. Current deep architectures, however, operate in an overparameterized regime where approximation error is not necessarily a bottleneck. To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable findings. First, we show that classical updates such as temporal difference (TD) learning or fitted-value-iteration (FVI) converge to different fixed points than residual minimization (RM) in the overparameterized linear case. We then develop a unified interpretation of overparameterized linear value estimation as minimizing the Euclidean norm of the weights subject to alternative constraints. A practical consequence is that RM can be modified by a simple alteration of the backup targets to obtain the same fixed points as FVI and TD (when they converge), while universally ensuring stability. Further, we provide an analysis of the generalization error of these methods, demonstrating per iterate bounds on the value prediction error of FVI, and fixed point bounds for TD and RM. Given this understanding, we then develop new algorithmic tools for improving recursive value estimation with deep models. In particular, we extract two regularizers that penalize out-of-span top-layer weights and co-linearity in top-layer features respectively. Empirically we find that these regularizers dramatically improve the stability of TD and FVI, while allowing RM to match and even sometimes surpass their generalization performance with assured stability. Ablations show that both regularizers are necessary to achieve these benefits, which persist between the fixed-policy and optimal-policy value estimation scenarios.
|
['Dale Schuurmans', 'Chris Harris', 'Ramki Gummadi', 'Oscar A Ramirez', 'Jincheng Mei', 'Bo Dai', 'Chenjun Xiao']
|
2021-09-29
| null | null | null |
iclr-2022-4
|
['value-prediction']
|
['computer-code']
|
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|
[4.24713659286499, 2.286999464035034]
|
fdd629e6-5641-4217-9ff3-cb4bc0bf464a
|
challenges-in-generalization-in-open-domain-1
| null | null |
https://openreview.net/forum?id=l6Pj9MziA0
|
https://openreview.net/pdf?id=l6Pj9MziA0
|
Challenges in Generalization in Open Domain Question Answering
|
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is as of yet unclear which aspects of novel questions that make them challenging. Drawing upon studies on systematic generalization, we introduce and annotate questions according to three categories that measure different levels and kinds of generalization: training set overlap, compositional generalization (comp-gen), and novel entity generalization (novel-entity). When evaluating six popular parametric and non-parametric models, we find that for the established Natural Questions and TriviaQA datasets, even the strongest model performance for comp-gen/novel-entity is 13.1/5.4% and 9.6/1.5% lower compared to that for the full test set – indicating the challenge posed by these types of questions. Furthermore, we show that whilst non-parametric models can handle questions containing novel entities, they struggle with those requiring compositional generalization. Through thorough analysis we find that key question difficulty factors are: cascading errors from the retrieval component, frequency of question pattern, and frequency of the entity.
|
['Anonymous']
|
2021-10-16
| null | null | null |
acl-arr-october-2021-10
|
['triviaqa', 'systematic-generalization']
|
['miscellaneous', 'reasoning']
|
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|
[11.182991027832031, 7.939867973327637]
|
3fdf6838-1c25-4e55-bc6b-6c440ca941e0
|
hierarchical-attention-based-age-estimation
|
2103.09882
| null |
https://arxiv.org/abs/2103.09882v1
|
https://arxiv.org/pdf/2103.09882v1.pdf
|
Hierarchical Attention-based Age Estimation and Bias Estimation
|
In this work we propose a novel deep-learning approach for age estimation based on face images. We first introduce a dual image augmentation-aggregation approach based on attention. This allows the network to jointly utilize multiple face image augmentations whose embeddings are aggregated by a Transformer-Encoder. The resulting aggregated embedding is shown to better encode the face image attributes. We then propose a probabilistic hierarchical regression framework that combines a discrete probabilistic estimate of age labels, with a corresponding ensemble of regressors. Each regressor is particularly adapted and trained to refine the probabilistic estimate over a range of ages. Our scheme is shown to outperform contemporary schemes and provide a new state-of-the-art age estimation accuracy, when applied to the MORPH II dataset for age estimation. Last, we introduce a bias analysis of state-of-the-art age estimation results.
|
['Yosi Keller', 'Shakediel Hiba']
|
2021-03-17
| null | null | null | null |
['age-estimation', 'age-estimation']
|
['computer-vision', 'miscellaneous']
|
[ 7.00713396e-02 4.56753969e-01 -1.85410589e-01 -9.89198744e-01
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|
[13.513839721679688, 0.8347854018211365]
|
c9d5fc4a-1144-4373-8dd5-ab7dec57dd50
|
from-intrinsic-to-counterfactual-on-the
|
2110.14844
| null |
https://arxiv.org/abs/2110.14844v1
|
https://arxiv.org/pdf/2110.14844v1.pdf
|
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
|
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models' input drive the final ranking decision, thus, they cannot often be understood by human stakeholders. In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable recommender systems without sacrificing their performance. In particular, we propose three types of explainable recommendation strategies with gradual change of model transparency: whitebox, graybox, and blackbox. Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. We apply these explainable models on five real-world data sets under the contextualized setting where users and items have explicit interactions. The empirical results show that our model achieves highly competitive ranking performance, and generates accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.
|
['Haixun Wang', 'Jingrui He', 'Haonan Wang', 'Yao Zhou']
|
2021-10-28
| null | null | null | null |
['explainable-models']
|
['computer-vision']
|
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|
[9.667428016662598, 5.699002742767334]
|
8a2579d9-6961-453e-9991-80071c9f4cbd
|
a-bio-inspired-implementation-of-a-sparse
|
2206.04924
| null |
https://arxiv.org/abs/2206.04924v1
|
https://arxiv.org/pdf/2206.04924v1.pdf
|
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model
|
The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that focuses on mimicking the basic principles that govern the brain in order to develop systems that achieve such computational capabilities. Within this field, bio-inspired learning and memory systems are still a challenge to be solved, and this is where the hippocampus is involved. It is the region of the brain that acts as a short-term memory, allowing the learning and unstructured and rapid storage of information from all the sensory nuclei of the cerebral cortex and its subsequent recall. In this work, we propose a novel bio-inspired memory model based on the hippocampus with the ability to learn memories, recall them from a cue (a part of the memory associated with the rest of the content) and even forget memories when trying to learn others with the same cue. This model has been implemented on the SpiNNaker hardware platform using Spiking Neural Networks, and a set of experiments and tests were performed to demonstrate its correct and expected operation. The proposed spike-based memory model generates spikes only when it receives an input, being energy efficient, and it needs 7 timesteps for the learning step and 6 timesteps for recalling a previously-stored memory. This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.
|
['Gabriel Jimenez-Moreno', 'Angel Jimenez-Fernandez', 'Juan P. Dominguez-Morales', 'Alvaro Ayuso-Martinez', 'Daniel Casanueva-Morato']
|
2022-06-10
| null | null | null | null |
['sparse-learning']
|
['methodology']
|
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|
[8.170656204223633, 2.538141965866089]
|
82eda09c-7947-48f6-828c-6d9ef0d01c58
|
universal-sketch-perceptual-grouping
| null | null |
http://openaccess.thecvf.com/content_ECCV_2018/html/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Ke_LI_Universal_Sketch_Perceptual_ECCV_2018_paper.pdf
|
Universal Sketch Perceptual Grouping
|
In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20,000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses. The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR).
|
['Tao Xiang', 'Yi-Zhe Song', 'Ke Li', 'Kaiyue Pang', 'Jifei Song', 'Timothy M. Hospedales', 'Honggang Zhang']
|
2018-09-01
| null | null | null |
eccv-2018-9
|
['sketch-based-image-retrieval']
|
['computer-vision']
|
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|
[11.67895221710205, 0.4986889958381653]
|
50b83f12-4541-4576-b441-eeddf606fba9
|
efficiently-mitigating-classification-bias
|
2010.12864
| null |
https://arxiv.org/abs/2010.12864v2
|
https://arxiv.org/pdf/2010.12864v2.pdf
|
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
|
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning. Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics. We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream fine-tuning. We find, in extensive experiments across hate speech detection, toxicity detection, occupation prediction, and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via fine-tuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model. Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning.
|
['Brendan Kennedy', 'Xiang Ren', 'Leonardo Neves', 'Aida Mostafazadeh Davani', 'Francesco Barbieri', 'Xisen Jin']
|
2020-10-24
| null |
https://aclanthology.org/2021.naacl-main.296
|
https://aclanthology.org/2021.naacl-main.296.pdf
|
naacl-2021-4
|
['occupation-prediction']
|
['natural-language-processing']
|
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|
[9.32372760772705, 10.117395401000977]
|
876f5aa1-8328-46c2-9e1c-644489e44174
|
one-trimap-video-matting
|
2207.13353
| null |
https://arxiv.org/abs/2207.13353v1
|
https://arxiv.org/pdf/2207.13353v1.pdf
|
One-Trimap Video Matting
|
Recent studies made great progress in video matting by extending the success of trimap-based image matting to the video domain. In this paper, we push this task toward a more practical setting and propose One-Trimap Video Matting network (OTVM) that performs video matting robustly using only one user-annotated trimap. A key of OTVM is the joint modeling of trimap propagation and alpha prediction. Starting from baseline trimap propagation and alpha prediction networks, our OTVM combines the two networks with an alpha-trimap refinement module to facilitate information flow. We also present an end-to-end training strategy to take full advantage of the joint model. Our joint modeling greatly improves the temporal stability of trimap propagation compared to the previous decoupled methods. We evaluate our model on two latest video matting benchmarks, Deep Video Matting and VideoMatting108, and outperform state-of-the-art by significant margins (MSE improvements of 56.4% and 56.7%, respectively). The source code and model are available online: https://github.com/Hongje/OTVM.
|
['Joon-Young Lee', 'Euntai Kim', 'Brian Price', 'Seoung Wug Oh', 'Hongje Seong']
|
2022-07-27
| null | null | null | null |
['image-matting', 'video-matting']
|
['computer-vision', 'computer-vision']
|
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|
[10.618520736694336, -0.8170952796936035]
|
0b22a53a-45c5-4000-b6d4-af9ef84b92ff
|
explain-your-move-understanding-agent-actions-1
|
1912.12191
| null |
https://arxiv.org/abs/1912.12191v4
|
https://arxiv.org/pdf/1912.12191v4.pdf
|
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
|
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see https://nikaashpuri.github.io/sarfa-saliency/.
|
['Sukriti Verma', 'Sameer Singh', 'Nikaash Puri', 'Piyush Gupta', 'Balaji Krishnamurthy', 'Shripad Deshmukh', 'Dhruv Kayastha']
|
2019-12-23
| null | null | null | null |
['board-games']
|
['playing-games']
|
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|
[4.020020008087158, 1.608374834060669]
|
aac2ede0-5907-48c2-ba99-4419bc3885e2
|
image-super-resolution-by-neural-texture
|
1903.00834
| null |
http://arxiv.org/abs/1903.00834v2
|
http://arxiv.org/pdf/1903.00834v2.pdf
|
Image Super-Resolution by Neural Texture Transfer
|
Due to the significant information loss in low-resolution (LR) images, it has
become extremely challenging to further advance the state-of-the-art of single
image super-resolution (SISR). Reference-based super-resolution (RefSR), on the
other hand, has proven to be promising in recovering high-resolution (HR)
details when a reference (Ref) image with similar content as that of the LR
input is given. However, the quality of RefSR can degrade severely when Ref is
less similar. This paper aims to unleash the potential of RefSR by leveraging
more texture details from Ref images with stronger robustness even when
irrelevant Ref images are provided. Inspired by the recent work on image
stylization, we formulate the RefSR problem as neural texture transfer. We
design an end-to-end deep model which enriches HR details by adaptively
transferring the texture from Ref images according to their textural
similarity. Instead of matching content in the raw pixel space as done by
previous methods, our key contribution is a multi-level matching conducted in
the neural space. This matching scheme facilitates multi-scale neural transfer
that allows the model to benefit more from those semantically related Ref
patches, and gracefully degrade to SISR performance on the least relevant Ref
inputs. We build a benchmark dataset for the general research of RefSR, which
contains Ref images paired with LR inputs with varying levels of similarity.
Both quantitative and qualitative evaluations demonstrate the superiority of
our method over state-of-the-art.
|
['Zhifei Zhang', 'Zhaowen Wang', 'Zhe Lin', 'Hairong Qi']
|
2019-03-03
|
image-super-resolution-by-neural-texture-1
|
http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Image_Super-Resolution_by_Neural_Texture_Transfer_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Image_Super-Resolution_by_Neural_Texture_Transfer_CVPR_2019_paper.pdf
|
cvpr-2019-6
|
['image-stylization', 'reference-based-super-resolution']
|
['computer-vision', 'computer-vision']
|
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|
[10.96084213256836, -2.080724000930786]
|
1f31d27e-0ca5-4042-814b-3360bc54a1bf
|
transformation-of-node-to-knowledge-graph
|
2111.09308
| null |
https://arxiv.org/abs/2111.09308v1
|
https://arxiv.org/pdf/2111.09308v1.pdf
|
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
|
Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation by developing embeddings of entities and relations into vector spaces. Graph embeddings encode the structural information present in a graph. The encoded embeddings then can be used to predict the missing links in a graph. However, obtaining the optimal embeddings for a graph can be a computationally challenging task specially in an embedded system. Two techniques which we focus on in this work are 1) node embeddings from random walk based methods and 2) knowledge graph embeddings. Random walk based embeddings are computationally inexpensive to obtain but are sub-optimal whereas knowledge graph embeddings perform better but are computationally expensive. In this work, we investigate a transformation model which converts node embeddings obtained from random walk based methods to embeddings obtained from knowledge graph methods directly without an increase in the computational cost. Extensive experimentation shows that the proposed transformation model can be used for solving link prediction in real-time.
|
['Minwoo Lee', 'Anant Kumar Mishra', 'Mayuri Deshpande', 'Archit Parnami']
|
2021-11-17
| null | null | null | null |
['knowledge-graph-embeddings', 'knowledge-graph-embeddings']
|
['graphs', 'methodology']
|
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|
[7.221368312835693, 6.249124050140381]
|
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