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752f795a-0de6-4a7f-bd6d-d4f214dace3c
|
dynamic-low-resolution-distillation-for-cost
|
2207.06694
| null |
https://arxiv.org/abs/2207.06694v2
|
https://arxiv.org/pdf/2207.06694v2.pdf
|
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
|
End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale has always been a tough trade-off since recognizing a small text instance usually requires enlarging the whole image, which brings high computational costs. In this paper, to address this problem, we propose a novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting framework, which aims to infer images in different small but recognizable resolutions and achieve a better balance between accuracy and efficiency. Concretely, we adopt a resolution selector to dynamically decide the input resolutions for different images, which is constraint by both inference accuracy and computational cost. Another sequential knowledge distillation strategy is conducted on the text recognition branch, making the low-res input obtains comparable performance to a high-res image. The proposed method can be optimized end-to-end and adopted in any current text spotting framework to improve the practicability. Extensive experiments on several text spotting benchmarks show that the proposed method vastly improves the usability of low-res models. The code is available at https://github.com/hikopensource/DAVAR-Lab-OCR/.
|
['Liang Qiao', 'Xi Li', 'Yi Niu', 'ShiLiang Pu', 'Zhanzhan Cheng', 'Ying Chen']
|
2022-07-14
| null | null | null | null |
['text-spotting']
|
['computer-vision']
|
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|
[12.006536483764648, 2.2228524684906006]
|
12de32e3-b071-4171-8bbb-9de2a14385c6
|
fuseformer-fusing-fine-grained-information-in
|
2109.02974
| null |
https://arxiv.org/abs/2109.02974v1
|
https://arxiv.org/pdf/2109.02974v1.pdf
|
FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
|
Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks. However, when used in video inpainting that requires fine-grained representation, existed method still suffers from yielding blurry edges in detail due to the hard patch splitting. Here we aim to tackle this problem by proposing FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations. The soft split divides feature map into many patches with given overlapping interval. On the contrary, the soft composition operates by stitching different patches into a whole feature map where pixels in overlapping regions are summed up. These two modules are first used in tokenization before Transformer layers and de-tokenization after Transformer layers, for effective mapping between tokens and features. Therefore, sub-patch level information interaction is enabled for more effective feature propagation between neighboring patches, resulting in synthesizing vivid content for hole regions in videos. Moreover, in FuseFormer, we elaborately insert the soft composition and soft split into the feed-forward network, enabling the 1D linear layers to have the capability of modelling 2D structure. And, the sub-patch level feature fusion ability is further enhanced. In both quantitative and qualitative evaluations, our proposed FuseFormer surpasses state-of-the-art methods. We also conduct detailed analysis to examine its superiority.
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['Hongsheng Li', 'Jifeng Dai', 'Xiaogang Wang', 'Wenxiu Sun', 'Lewei Lu', 'Xiaoyu Shi', 'Yangyi Huang', 'Hanming Deng', 'Rui Liu']
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2021-09-07
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http://openaccess.thecvf.com//content/ICCV2021/html/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.html
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http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.pdf
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iccv-2021-1
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['seeing-beyond-the-visible', 'video-inpainting']
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['computer-vision', 'computer-vision']
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|
[10.801051139831543, -1.5072343349456787]
|
6511673d-c67e-46ef-85bb-d9e904a340ce
|
artifact-identification-in-x-ray-diffraction
|
2207.14804
| null |
https://arxiv.org/abs/2207.14804v1
|
https://arxiv.org/pdf/2207.14804v1.pdf
|
Artifact Identification in X-ray Diffraction Data using Machine Learning Methods
|
The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample environments (e.g., diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern, along with detailed analysis such as Rietveld refinement which indicates how the measured structure deviates from the ideal structure (e.g., internal stresses or defects). For in situ experiments, a series of XRD images is usually collected on the same sample at different conditions (e.g., adiabatic conditions), yielding different states of matter, or simply collected continuously as a function of time to track the change of a sample over a chemical or physical process. In situ experiments are usually performed with area detectors, collecting 2D images composed of diffraction rings for ideal powders. Depending on the material's form, one may observe different characteristics other than the typical Debye Scherrer rings for a realistic sample and its environments, such as textures or preferred orientations and single crystal diffraction spots in the 2D XRD image. In this work, we present an investigation of machine learning methods for fast and reliable identification and separation of the single crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. We observe that the gradient boosting method can consistently produce high accuracy results when it is trained with small subsets of highly diverse datasets. The method dramatically decreases the amount of time spent on identifying and separating single crystal spots in comparison to the conventional method.
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['Nicholas Schwarz', 'Uta Ruett', 'Wenqian Xu', 'Hannah Parraga', 'James Weng', 'Howard Yanxon']
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2022-07-29
| null | null | null | null |
['machine-learning', 'machine-learning']
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['methodology', 'miscellaneous']
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|
[5.251382827758789, 5.173381805419922]
|
0755d911-ffd4-46ec-9e24-fddf8386dce1
|
identifying-suspicious-regions-of-covid-19-by
|
2303.14901
| null |
https://arxiv.org/abs/2303.14901v1
|
https://arxiv.org/pdf/2303.14901v1.pdf
|
Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
|
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volumes, however, because of the heavy workload, an automatic analysis method using a computer is desired. Most computer-aided diagnosis studies have addressed only a portion of the elements necessary for the identification. In this work, we realize the identification method through a classification task by using a 2.5-dimensional CNN with three-dimensional attention mechanisms. We visualize the suspicious regions by applying a backpropagation based on positive gradients to attention-weighted features. We perform experiments on an in-house dataset and two public datasets to reveal the generalization ability of the proposed method. The proposed architecture achieved AUCs of over 0.900 for all the datasets, and mean sensitivity $0.853 \pm 0.036$ and specificity $0.870 \pm 0.040$. The method can also identify notable lesions pointed out in the radiology report as suspicious regions.
|
['Kensaku MORI', 'Shigeki Aoki', 'Toshiaki Akashi', 'Masahiro Hashimoto', 'Yoshito Otake', 'Yuichiro Hayashi', 'Masahiro Oda', 'Hayato Itoh', 'Ryo Toda']
|
2023-03-27
| null | null | null | null |
['specificity']
|
['natural-language-processing']
|
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|
[15.56490421295166, -1.7048465013504028]
|
bd94b0a1-b346-4e36-8548-9baf6d49d86d
|
face-hallucination-revisited-an-exploratory
|
1812.0901
| null |
http://arxiv.org/abs/1812.09010v1
|
http://arxiv.org/pdf/1812.09010v1.pdf
|
Face Hallucination Revisited: An Exploratory Study on Dataset Bias
|
Contemporary face hallucination (FH) models exhibit considerable ability to
reconstruct high-resolution (HR) details from low-resolution (LR) face images.
This ability is commonly learned from examples of corresponding HR-LR image
pairs, created by artificially down-sampling the HR ground truth data. This
down-sampling (or degradation) procedure not only defines the characteristics
of the LR training data, but also determines the type of image degradations the
learned FH models are eventually able to handle. If the image characteristics
encountered with real-world LR images differ from the ones seen during
training, FH models are still expected to perform well, but in practice may not
produce the desired results. In this paper we study this problem and explore
the bias introduced into FH models by the characteristics of the training data.
We systematically analyze the generalization capabilities of several FH models
in various scenarios, where the image the degradation function does not match
the training setup and conduct experiments with synthetically downgraded as
well as real-life low-quality images. We make several interesting findings that
provide insight into existing problems with FH models and point to future
research directions.
|
['Vitomir Štruc', 'Simon Dobrišek', 'Martin Pernuš', 'Leo Cluzel', 'Walter Scheirer', 'Klemen Grm']
|
2018-12-21
| null | null | null | null |
['face-hallucination']
|
['computer-vision']
|
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|
[12.782113075256348, -0.1718812733888626]
|
a0d426c8-0dfb-4461-b637-b824b840db58
|
multiplex-heterogeneous-graph-convolutional
|
2208.06129
| null |
https://arxiv.org/abs/2208.06129v1
|
https://arxiv.org/pdf/2208.06129v1.pdf
|
Multiplex Heterogeneous Graph Convolutional Network
|
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics.
|
['Junyu Dong', 'Zhongying Zhao', 'Chao Huang', 'Yanwei Yu', 'Chaofan Fu', 'Pengyang Yu']
|
2022-08-12
| null | null | null | null |
['network-embedding']
|
['methodology']
|
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|
[7.215280055999756, 6.2893548011779785]
|
a5406fd9-4719-448a-aeaa-9be334481dab
|
sparse-representer-theorems-for-learning-in
|
2305.12584
| null |
https://arxiv.org/abs/2305.12584v1
|
https://arxiv.org/pdf/2305.12584v1.pdf
|
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
|
Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, which represents the extreme points of the solution set by a linear combination of the extreme points of the subdifferential set, of the norm function, which is data-dependent. We then propose sufficient conditions on the RKBS that can transform the explicit representation of the solutions to a sparse kernel representation having fewer terms than the number of the observed data. Under the proposed sufficient conditions, we investigate the role of the regularization parameter on sparsity of the regularized solutions. We further show that two specific RKBSs: the sequence space $\ell_1(\mathbb{N})$ and the measure space can have sparse representer theorems for both MNI and regularization models.
|
['Mingsong Yan', 'Yuesheng Xu', 'Rui Wang']
|
2023-05-21
| null | null | null | null |
['sparse-learning']
|
['methodology']
|
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|
[7.241813659667969, 4.195682048797607]
|
e336b211-f6ef-44b6-b42e-4995e406f02b
|
zero-pronoun-resolution-with-attention-based
| null | null |
https://aclanthology.org/C18-1002
|
https://aclanthology.org/C18-1002.pdf
|
Zero Pronoun Resolution with Attention-based Neural Network
|
Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utilize contexts of the zero pronouns, we here introduce the self-attention mechanism for encoding zero pronouns. With the help of the multiple hops of attention, our model is able to focus on some informative parts of the associated texts and therefore produces an efficient way of encoding the zero pronouns. In addition, an attention-based recurrent neural network is proposed for encoding candidate antecedents by their contents. Experiment results are encouraging: our proposed attention-based model gains the best performance on the Chinese portion of the OntoNotes corpus, substantially surpasses existing Chinese zero pronoun resolution baseline systems.
|
['Wei-Nan Zhang', 'Yu Zhang', 'William Yang Wang', 'Ting Liu', 'Qingyu Yin']
|
2018-08-01
|
zero-pronoun-resolution-with-attention-based-1
|
https://aclanthology.org/C18-1002
|
https://aclanthology.org/C18-1002.pdf
|
coling-2018-8
|
['chinese-zero-pronoun-resolution']
|
['natural-language-processing']
|
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|
[10.346763610839844, 9.266100883483887]
|
96896f29-f905-4b6a-b93d-e6a4efa0965c
|
feature-based-decipherment-for-large
|
1508.02142
| null |
http://arxiv.org/abs/1508.02142v1
|
http://arxiv.org/pdf/1508.02142v1.pdf
|
Feature-based Decipherment for Large Vocabulary Machine Translation
|
Orthographic similarities across languages provide a strong signal for
probabilistic decipherment, especially for closely related language pairs. The
existing decipherment models, however, are not well-suited for exploiting these
orthographic similarities. We propose a log-linear model with latent variables
that incorporates orthographic similarity features. Maximum likelihood training
is computationally expensive for the proposed log-linear model. To address this
challenge, we perform approximate inference via MCMC sampling and contrastive
divergence. Our results show that the proposed log-linear model with
contrastive divergence scales to large vocabularies and outperforms the
existing generative decipherment models by exploiting the orthographic
features.
|
['Iftekhar Naim', 'Daniel Gildea']
|
2015-08-10
| null | null | null | null |
['decipherment']
|
['natural-language-processing']
|
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|
[11.085809707641602, 9.555103302001953]
|
3221a1dd-b446-4c74-9ff9-02361c03671e
|
cross-lingual-dialogue-dataset-creation-via
|
2201.13405
| null |
https://arxiv.org/abs/2201.13405v1
|
https://arxiv.org/pdf/2201.13405v1.pdf
|
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
|
Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, the potential of this technology is not fully realised, as current datasets for multilingual ToD - both for modular and end-to-end modelling - suffer from severe limitations. 1) When created from scratch, they are usually small in scale and fail to cover many possible dialogue flows. 2) Translation-based ToD datasets might lack naturalness and cultural specificity in the target language. In this work, to tackle these limitations we propose a novel outline-based annotation process for multilingual ToD datasets, where domain-specific abstract schemata of dialogue are mapped into natural language outlines. These in turn guide the target language annotators in writing a dialogue by providing instructions about each turn's intents and slots. Through this process we annotate a new large-scale dataset for training and evaluation of multilingual and cross-lingual ToD systems. Our Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding, dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. Qualitative and quantitative analyses of COD versus an equivalent translation-based dataset demonstrate improvements in data quality, unlocked by the outline-based approach. Finally, we benchmark a series of state-of-the-art systems for cross-lingual ToD, setting reference scores for future work and demonstrating that COD prevents over-inflated performance, typically met with prior translation-based ToD datasets.
|
['Anna Korhonen', 'Ivan Vulić', 'Edoardo Maria Ponti', 'Evgeniia Razumovskaia', 'Olga Majewska']
|
2022-01-31
| null | null | null | null |
['end-to-end-dialogue-modelling']
|
['natural-language-processing']
|
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|
[12.65090274810791, 8.215751647949219]
|
4967a996-ef60-41bc-8c84-efe0c1fb9bc0
|
retrosynthesis-with-attention-based-nmt-model
|
1908.00727
| null |
https://arxiv.org/abs/1908.00727v1
|
https://arxiv.org/pdf/1908.00727v1.pdf
|
Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions
|
We cast retrosynthesis as a machine translation problem by introducing a special Tensor2Tensor, an entire attention-based and fully data-driven model. Given a data set comprising about 50,000 diverse reactions extracted from USPTO patents, the model significantly outperforms seq2seq model (34.7%) on a top-1 accuracy by achieving 54.1%. For yielding better results, parameters such as batch size and training time are thoroughly investigated to train the model. Additionally, we offer a novel insight into the causes of grammatically invalid SMILES, and conduct a test in which experienced chemists pick out and analyze the "wrong" predictions that may be chemically plausible but differ from the ground truth. Actually, the effectiveness of our model is un-derestimated and the "true" top-1 accuracy can reach to 64.6%.
|
['Ling Wang', 'Hongliang Duan', 'Jianjun Li', 'Chengyun Zhang']
|
2019-08-02
| null | null | null | null |
['retrosynthesis']
|
['medical']
|
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|
[4.5033278465271, 6.101730823516846]
|
5cf7891b-0909-4442-be0d-543dc5faf873
|
improving-sequence-to-sequence-pre-training
|
2101.00416
| null |
https://arxiv.org/abs/2101.00416v2
|
https://arxiv.org/pdf/2101.00416v2.pdf
|
Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting
|
In this paper, we generalize text infilling (e.g., masked language models) by proposing Sequence Span Rewriting (SSR) as a self-supervised sequence-to-sequence (seq2seq) pre-training objective. SSR provides more fine-grained learning signals for text representations by supervising the model to rewrite imperfect spans to ground truth, and it is more consistent than text infilling with many downstream seq2seq tasks that rewrite a source sentences into a target sentence. Our experiments with T5 models on various seq2seq tasks show that SSR can substantially improve seq2seq pre-training. Moreover, we observe SSR is especially helpful to improve pre-training a small-size seq2seq model with a powerful imperfect span generator, which indicates a new perspective of transferring knowledge from a large model to a smaller model for seq2seq pre-training.
|
['Ke Xu', 'Canwen Xu', 'Furu Wei', 'Tao Ge', 'Wangchunshu Zhou']
|
2021-01-02
| null |
https://aclanthology.org/2021.emnlp-main.45
|
https://aclanthology.org/2021.emnlp-main.45.pdf
|
emnlp-2021-11
|
['text-infilling']
|
['natural-language-processing']
|
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|
[11.660324096679688, 9.031847953796387]
|
76d6728a-3995-45b6-8155-c794c96406db
|
automatically-summarizing-evidence-from
|
2303.05392
| null |
https://arxiv.org/abs/2303.05392v1
|
https://arxiv.org/pdf/2303.05392v1.pdf
|
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
|
We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs.
|
['Byron C. Wallace', 'Iain J. Marshal', 'Denis Jered McInerney', 'Sanjana Ramprasad']
|
2023-03-07
| null | null | null | null |
['multi-document-summarization', 'document-summarization']
|
['natural-language-processing', 'natural-language-processing']
|
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|
[12.29055404663086, 9.607202529907227]
|
8cd03f56-211d-46fa-9fd6-8b89c5b8faaf
|
maximum-class-separation-as-inductive-bias-in
|
2206.08704
| null |
https://arxiv.org/abs/2206.08704v2
|
https://arxiv.org/pdf/2206.08704v2.pdf
|
Maximum Class Separation as Inductive Bias in One Matrix
|
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative solutions have been proposed through differential optimization. Current approaches tend to optimize classification and separation jointly: aligning inputs with class vectors and separating class vectors angularly. This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations. The main observation behind our approach is that separation does not require optimization but can be solved in closed-form prior to training and plugged into a network. We outline a recursive approach to obtain the matrix consisting of maximally separable vectors for any number of classes, which can be added with negligible engineering effort and computational overhead. Despite its simple nature, this one matrix multiplication provides real impact. We show that our proposal directly boosts classification, long-tailed recognition, out-of-distribution detection, and open-set recognition, from CIFAR to ImageNet. We find empirically that maximum separation works best as a fixed bias; making the matrix learnable adds nothing to the performance. The closed-form implementation and code to reproduce the experiments are available on github.
|
['Pascal Mettes', 'Rita Cucchiara', 'Elise van der Pol', 'Max van Spengler', 'Gertjan J. Burghouts', 'Tejaswi Kasarla']
|
2022-06-17
| null | null | null | null |
['open-set-learning']
|
['miscellaneous']
|
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|
[8.794456481933594, 3.289363145828247]
|
19071d06-d090-41b6-b481-efe7efc628bc
|
towards-more-realistic-generation-of
|
2205.12609
| null |
https://arxiv.org/abs/2205.12609v2
|
https://arxiv.org/pdf/2205.12609v2.pdf
|
Generating Information-Seeking Conversations from Unlabeled Documents
|
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants. In our baseline SIMSEEK-SYM, a questioner generates follow-up questions upon the predetermined answer by an answerer. On the contrary, SIMSEEK-ASYM first generates the question and then finds its corresponding answer under the conversational context. Our experiments show that they can synthesize effective training resources for CQA and conversational search tasks. As a result, conversations from SIMSEEK-ASYM not only make more improvements in our experiments but also are favorably reviewed in a human evaluation. We finally release a large-scale resource of synthetic conversations, WIKI-SIMSEEK, containing 2 million CQA pairs built upon Wikipedia documents. With the dataset, our CQA model achieves state-of-the-art performance on a recent CQA benchmark, QuAC.
|
['Jaewoo Kang', 'Kang Min Yoo', 'Sungdong Kim', 'Gangwoo Kim']
|
2022-05-25
| null | null | null | null |
['conversational-search']
|
['natural-language-processing']
|
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|
[12.017929077148438, 7.928141117095947]
|
a89aed86-9b30-47f7-a392-ef8ff2c76955
|
conversational-question-answering-over
|
2004.13117
| null |
https://arxiv.org/abs/2004.13117v3
|
https://arxiv.org/pdf/2004.13117v3.pdf
|
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
|
Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans conversing with each other, it introduces two key research challenges: understanding the context left implicit by the user in follow-up questions, and dealing with ad hoc question formulations. In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns. To this end, CROWN first builds a word proximity network (WPN) from large corpora to store statistically significant term co-occurrences. At answering time, passages are ranked by a combination of their similarity to the question, and coherence of query terms within: these factors are measured by reading off node and edge weights from the WPN. CROWN provides an interface that is both intuitive for end-users, and insightful for experts for reconfiguration to individual setups. CROWN was evaluated on TREC CAsT data, where it achieved above-median performance in a pool of neural methods.
|
['Magdalena Kaiser', 'Rishiraj Saha Roy', 'Gerhard Weikum']
|
2020-04-27
| null | null | null | null |
['passage-ranking']
|
['natural-language-processing']
|
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|
[11.994791030883789, 7.862964630126953]
|
478ad947-803b-49dd-b261-cdbbb6e1a95a
|
imagenet-training-in-minutes
|
1709.05011
| null |
http://arxiv.org/abs/1709.05011v10
|
http://arxiv.org/pdf/1709.05011v10.pdf
|
ImageNet Training in Minutes
|
Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU
takes 14 days. This training requires 10^18 single precision operations in
total. On the other hand, the world's current fastest supercomputer can finish
2 * 10^17 single precision operations per second (Dongarra et al 2017,
https://www.top500.org/lists/2017/06/). If we can make full use of the
supercomputer for DNN training, we should be able to finish the 90-epoch
ResNet-50 training in one minute. However, the current bottleneck for fast DNN
training is in the algorithm level. Specifically, the current batch size (e.g.
512) is too small to make efficient use of many processors. For large-scale DNN
training, we focus on using large-batch data-parallelism synchronous SGD
without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman,
Ginsburg, 2017, arXiv:1708.03888) enables us to scale the batch size to
extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with
AlexNet in 11 minutes on 1024 CPUs. About three times faster than Facebook's
result (Goyal et al 2017, arXiv:1706.02677), we finish the 90-epoch ImageNet
training with ResNet-50 in 20 minutes on 2048 KNLs without losing accuracy.
State-of-the-art ImageNet training speed with ResNet-50 is 74.9% top-1 test
accuracy in 15 minutes. We got 74.9% top-1 test accuracy in 64 epochs, which
only needs 14 minutes. Furthermore, when we increase the batch size to above
16K, our accuracy is much higher than Facebook's on corresponding batch sizes.
Our source code is available upon request.
|
['Cho-Jui Hsieh', 'Zhao Zhang', 'Yang You', 'Kurt Keutzer', 'James Demmel']
|
2017-09-14
| null | null | null | null |
['2048']
|
['playing-games']
|
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|
[8.534956932067871, 3.051907777786255]
|
86336f23-f06b-4c9e-b328-c319feaa9918
|
long-term-stability-and-generalization-of
|
2205.04601
| null |
https://arxiv.org/abs/2205.04601v1
|
https://arxiv.org/pdf/2205.04601v1.pdf
|
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
|
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a data-driven weather model is not available in realistic scenarios. Finally, deterministic data-driven forecasting models suffer from issues with long-term stability and unphysical climate drift, which makes these data-driven models unsuitable for computing climate statistics. Given these challenges, previous studies have tried to pre-train deep learning-based weather forecasting models on a large amount of imperfect long-term climate model simulations and then re-train them on available observational data. In this paper, we propose a convolutional variational autoencoder-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a 2-layer quasi-geostrophic flow and re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation. This re-trained model then performs stochastic forecasting with a noisy initial condition sampled from the perfect simulation. We show that our ensemble-based stochastic data-driven model outperforms a baseline deterministic encoder-decoder-based convolutional model in terms of short-term skills while remaining stable for long-term climate simulations yielding accurate climatology.
|
['Pedram Hassanzadeh', 'Wahid Bhimji', 'Ebrahim Nabizadeh', 'Jaideep Pathak', 'Ashesh Chattopadhyay']
|
2022-05-09
| null | null | null | null |
['weather-forecasting']
|
['miscellaneous']
|
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|
[6.565045356750488, 2.9575955867767334]
|
35aaeb7a-a4a8-431e-a3f5-d0a7c7928b0b
|
uit-saviors-at-medvqa-gi-2023-improving
|
2307.02783
| null |
https://arxiv.org/abs/2307.02783v1
|
https://arxiv.org/pdf/2307.02783v1.pdf
|
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering
|
In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.
|
['Thien T. B. Nguyen', 'Linh N. P. Bui', 'Hao K. Tieu', 'Anh T. Vo', 'Triet M. Thai']
|
2023-07-06
| null | null | null | null |
['visual-question-answering', 'visual-question-answering-1', 'image-enhancement', 'question-answering']
|
['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing']
|
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-1.34124115e-01 2.45604247e-01 6.38414323e-01 6.27517223e-01
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|
[11.072051048278809, 1.6182829141616821]
|
d6ba4d33-6b44-4f1c-b7f2-3956107774bb
|
muxconv-information-multiplexing-in
|
2003.1388
| null |
https://arxiv.org/abs/2003.13880v2
|
https://arxiv.org/pdf/2003.13880v2.pdf
|
MUXConv: Information Multiplexing in Convolutional Neural Networks
|
Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years. A key driving force has been the idea of trading-off model expressivity and efficiency through a combination of $1\times 1$ and depth-wise separable convolutions in lieu of a standard convolutional layer. The price of the efficiency, however, is the sub-optimal flow of information across space and channels in the network. To overcome this limitation, we present MUXConv, a layer that is designed to increase the flow of information by progressively multiplexing channel and spatial information in the network, while mitigating computational complexity. Furthermore, to demonstrate the effectiveness of MUXConv, we integrate it within an efficient multi-objective evolutionary algorithm to search for the optimal model hyper-parameters while simultaneously optimizing accuracy, compactness, and computational efficiency. On ImageNet, the resulting models, dubbed MUXNets, match the performance (75.3% top-1 accuracy) and multiply-add operations (218M) of MobileNetV3 while being 1.6$\times$ more compact, and outperform other mobile models in all the three criteria. MUXNet also performs well under transfer learning and when adapted to object detection. On the ChestX-Ray 14 benchmark, its accuracy is comparable to the state-of-the-art while being $3.3\times$ more compact and $14\times$ more efficient. Similarly, detection on PASCAL VOC 2007 is 1.2% more accurate, 28% faster and 6% more compact compared to MobileNetV2. Code is available from https://github.com/human-analysis/MUXConv
|
['Zhichao Lu', 'Kalyanmoy Deb', 'Vishnu Naresh Boddeti']
|
2020-03-31
|
muxconv-information-multiplexing-in-1
|
http://openaccess.thecvf.com/content_CVPR_2020/html/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.pdf
|
cvpr-2020-6
|
['pneumonia-detection']
|
['medical']
|
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5.03713846e-01 1.03234959e+00 3.41874927e-01 -7.64007568e-01
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|
[8.594230651855469, 2.859525442123413]
|
815d8a52-cb16-46b8-9c64-61dd4901c8b3
|
efficient-deep-models-for-real-time-4k-image
| null | null |
https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Conde_Efficient_Deep_Models_for_Real-Time_4K_Image_Super-Resolution._NTIRE_2023_CVPRW_2023_paper.html
|
https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/papers/Conde_Efficient_Deep_Models_for_Real-Time_4K_Image_Super-Resolution._NTIRE_2023_CVPRW_2023_paper.pdf
|
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report
|
This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (x2 and x3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR.
|
['and others', 'Daniel Motilla', 'Radu Timofte', 'Eduard Zamfir', 'Marcos V. Conde']
|
2023-06-01
| null | null | null |
cvprw-2023-6
|
['image-super-resolution', 'super-resolution']
|
['computer-vision', 'computer-vision']
|
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|
[10.98399543762207, -2.0754001140594482]
|
d697c76c-dc8b-473b-bc6b-a302c7980b96
|
hdmapnet-an-online-hd-map-construction-and
|
2107.06307
| null |
https://arxiv.org/abs/2107.06307v4
|
https://arxiv.org/pdf/2107.06307v4.pdf
|
HDMapNet: An Online HD Map Construction and Evaluation Framework
|
Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which dynamically constructs the local semantics based on onboard sensor observations. Meanwhile, we introduce a semantic map learning method, dubbed HDMapNet. HDMapNet encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements in the bird's-eye view. We benchmark HDMapNet on nuScenes dataset and show that in all settings, it performs better than baseline methods. Of note, our camera-LiDAR fusion-based HDMapNet outperforms existing methods by more than 50% in all metrics. In addition, we develop semantic-level and instance-level metrics to evaluate the map learning performance. Finally, we showcase our method is capable of predicting a locally consistent map. By introducing the method and metrics, we invite the community to study this novel map learning problem.
|
['Hang Zhao', 'Yilun Wang', 'Yue Wang', 'Qi Li']
|
2021-07-13
| null | null | null | null |
['hd-semantic-map-learning']
|
['computer-vision']
|
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5.61291337e-01 2.75206685e-01 5.04448116e-01 -8.47670913e-01
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|
[7.934122085571289, -1.996225357055664]
|
175dab04-bf67-4e85-8bf2-cf0031998046
|
seeing-the-wood-for-the-trees-a-contrastive
| null | null |
https://aclanthology.org/2022.findings-naacl.82
|
https://aclanthology.org/2022.findings-naacl.82.pdf
|
Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering
|
Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB. Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing components (. key entities and/or relations) may be absent for question answering. To effectively address this problem, we propose a contrastive regularization based method, which is motivated by the learn-by-analogy capability from human readers.Specifically, the proposed work includes two major modules: the knowledge extension and sMoCo module. The former aims at exploiting the latent knowledge from the context KB and generating auxiliary information in the form of question-answer pairs. The later module utilizes those additional pairs and applies the contrastive regularization to learn informative representations, that making hard positive pairs attracted and hard negative pairs separated. Empirically, we achieved the state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.
|
['Yi Guo', 'Jack Yang', 'Xun Yao', 'Xinrong Hu', 'Shijie Mei', 'Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn']
| null | null | null | null |
findings-naacl-2022-7
|
['knowledge-base-question-answering']
|
['natural-language-processing']
|
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|
[10.778676986694336, 7.965538501739502]
|
06325e7b-8ce5-4d6f-bd61-b57070b413cb
|
polarity-based-sarcasm-detection-using
|
2304.01424
| null |
https://arxiv.org/abs/2304.01424v1
|
https://arxiv.org/pdf/2304.01424v1.pdf
|
Polarity based Sarcasm Detection using Semigraph
|
Sarcasm is an advanced linguistic expression often found on various online platforms. Sarcasm detection is challenging in natural language processing tasks that affect sentiment analysis. This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes. A variation of the semigraph is suggested in the pattern-relatedness of the text document. The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph. The sarcastic polarity score represents the possibility that a document will become sarcastic. Sarcasm is detected based on the polarity scoring model. The performance of the proposed model enhances the existing prior art approach to sarcasm detection. In the Amazon product review, the model achieved the accuracy, recall, and f-measure of 0.87, 0.79, and 0.83, respectively.
|
['Vaibhav Khatavkar', 'Swapnil Mane']
|
2023-04-04
| null | null | null | null |
['sarcasm-detection']
|
['natural-language-processing']
|
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|
[9.108311653137207, 10.56945514678955]
|
70286a42-4d57-44a0-8871-6c14831449a8
|
dblface-domain-based-labels-for-nir-vis
|
2010.03771
| null |
https://arxiv.org/abs/2010.03771v1
|
https://arxiv.org/pdf/2010.03771v1.pdf
|
DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition
|
Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Label Face (DBLFace), a learning approach based on the assumption that a subject is not represented by a single label but by a set of labels. Each label represents images of a specific domain. In particular, a set of two labels per subject, one for the NIR images and one for the VIS images, are used for training a NIR-VIS face recognition model. The classification of images into different domains reduces the intra-class variation and lessens the negative impact of data imbalance in training. To train a network with sets of labels, we introduce a domain-based angular margin loss and a maximum angular loss to maintain the inter-class discrepancy and to enforce the close relationship of labels in a set. Quantitative experiments confirm that DBLFace significantly improves the rank-1 identification rate by 6.7% on the EDGE20 dataset and achieves state-of-the-art performance on the CASIA NIR-VIS 2.0 dataset.
|
['Ioannis A. Kakadiaris', 'Ha Le']
|
2020-10-08
| null | null | null | null |
['heterogeneous-face-recognition']
|
['computer-vision']
|
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-3.01140308e-01 -2.39578933e-01 8.62736344e-01 7.22715795e-01
4.81949806e-01 3.49967122e-01 1.21559799e+00 -3.96140695e-01
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|
[13.160568237304688, 0.5396853685379028]
|
ace3e43e-e364-4915-93b4-1eada0208f9d
|
knowledge-graph-empowered-entity-description
|
2004.14813
| null |
https://arxiv.org/abs/2004.14813v2
|
https://arxiv.org/pdf/2004.14813v2.pdf
|
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
|
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.
|
['Zhanming Jie', 'Liying Cheng', 'Dekun Wu', 'Yan Zhang', 'Luo Si', 'Wei Lu', 'Lidong Bing']
|
2020-04-30
| null |
https://aclanthology.org/2020.emnlp-main.90
|
https://aclanthology.org/2020.emnlp-main.90.pdf
|
emnlp-2020-11
|
['graph-to-sequence', 'kg-to-text']
|
['natural-language-processing', 'natural-language-processing']
|
[ 6.71521500e-02 7.80426025e-01 -4.57962036e-01 -3.08501780e-01
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|
[9.453425407409668, 8.118066787719727]
|
aad0cc10-fd97-440c-8325-459c44c910a7
|
walking-on-thin-air-environment-free-physics
|
1812.01203
| null |
http://arxiv.org/abs/1812.01203v1
|
http://arxiv.org/pdf/1812.01203v1.pdf
|
Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture
|
We propose a generative approach to physics-based motion capture. Unlike
prior attempts to incorporate physics into tracking that assume the subject and
scene geometry are calibrated and known a priori, our approach is automatic and
online. This distinction is important since calibration of the environment is
often difficult, especially for motions with props, uneven surfaces, or outdoor
scenes. The use of physics in this context provides a natural framework to
reason about contact and the plausibility of recovered motions. We propose a
fast data-driven parametric body model, based on linear-blend skinning, which
decouples deformations due to pose, anthropometrics and body shape. Pose (and
shape) parameters are estimated using robust ICP optimization with
physics-based dynamic priors that incorporate contact. Contact is estimated
from torque trajectories and predictions of which contact points were active.
To our knowledge, this is the first approach to take physics into account
without explicit {\em a priori} knowledge of the environment or body
dimensions. We demonstrate effective tracking from a noisy single depth camera,
improving on state-of-the-art results quantitatively and producing better
qualitative results, reducing visual artifacts like foot-skate and jitter.
|
['Marcus A. Brubaker', 'Micha Livne', 'Leonid Sigal', 'David J. Fleet']
|
2018-12-04
| null | null | null | null |
['markerless-motion-capture']
|
['computer-vision']
|
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|
[7.093757629394531, -1.023489236831665]
|
69d2d5aa-b9c5-4ad9-ae88-855d1df64bfb
|
spelling-correction-using-phonetics-in-e
| null | null |
https://aclanthology.org/2022.ecnlp-1.9
|
https://aclanthology.org/2022.ecnlp-1.9.pdf
|
Spelling Correction using Phonetics in E-commerce Search
|
In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., “bluetooth sound system” vs. “blutut sant sistam”) with numerous noisy forms and sparse occurrences. In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost. Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+. This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.
|
['Yi Sun', 'Jingyuan Deng', 'Jia Liu', 'Yan Gao', 'Yifei Teng', 'Alireza Bagheri Garakani', 'Fan Yang']
| null | null | null | null |
ecnlp-acl-2022-5
|
['spelling-correction']
|
['natural-language-processing']
|
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|
[10.890271186828613, 10.645601272583008]
|
f9c8d0ae-7c30-47b2-b79a-4220d2bd09af
|
analysis-of-risk-factor-domains-in-psychosis
|
1809.05752
| null |
http://arxiv.org/abs/1809.05752v1
|
http://arxiv.org/pdf/1809.05752v1.pdf
|
Analysis of Risk Factor Domains in Psychosis Patient Health Records
|
Readmission after discharge from a hospital is disruptive and costly,
regardless of the reason. However, it can be particularly problematic for
psychiatric patients, so predicting which patients may be readmitted is
critically important but also very difficult. Clinical narratives in
psychiatric electronic health records (EHRs) span a wide range of topics and
vocabulary; therefore, a psychiatric readmission prediction model must begin
with a robust and interpretable topic extraction component. We created a data
pipeline for using document vector similarity metrics to perform topic
extraction on psychiatric EHR data in service of our long-term goal of creating
a readmission risk classifier. We show initial results for our topic extraction
model and identify additional features we will be incorporating in the future.
|
['Mei-Hua Hall', 'Philip Cawkwell', 'Marie Meteer', 'Eben Holderness', 'Nicholas Miller', 'Kirsten Bolton', 'James Pustejovsky']
|
2018-09-15
|
analysis-of-risk-factor-domains-in-psychosis-1
|
https://aclanthology.org/W18-5615
|
https://aclanthology.org/W18-5615.pdf
|
ws-2018-10
|
['readmission-prediction']
|
['medical']
|
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|
[8.46728515625, 8.434021949768066]
|
5e981eae-27f1-4916-80cf-746f8fe70c4c
|
compm-context-modeling-with-speakers-pre
| null | null |
https://aclanthology.org/2022.naacl-main.416
|
https://aclanthology.org/2022.naacl-main.416.pdf
|
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation
|
As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, many studies reflect the dialogue context to improve the performances. Many recent approaches show performance improvement by combining knowledge into modules learned from external structured data. However, structured data is difficult to access in non-English languages, making it difficult to extend to other languages. Therefore, we extract the pre-trained memory using the pre-trained language model as an extractor of external knowledge. We introduce CoMPM, which combines the speaker’s pre-trained memory with the context model, and find that the pre-trained memory significantly improves the performance of the context model. CoMPM achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data. In addition, our method shows that it can be extended to other languages because structured knowledge is not required, unlike previous methods. Our code is available on github .
|
['Wooin Lee', 'Joosung Lee']
| null | null | null | null |
naacl-2022-7
|
['emotion-recognition-in-conversation']
|
['natural-language-processing']
|
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|
[12.98874568939209, 6.257452487945557]
|
08a96edf-642e-4c25-bcdf-cfc1b735147c
|
poisoning-behavioral-malware-clustering
|
1811.09985
| null |
http://arxiv.org/abs/1811.09985v1
|
http://arxiv.org/pdf/1811.09985v1.pdf
|
Poisoning Behavioral Malware Clustering
|
Clustering algorithms have become a popular tool in computer security to
analyze the behavior of malware variants, identify novel malware families, and
generate signatures for antivirus systems. However, the suitability of
clustering algorithms for security-sensitive settings has been recently
questioned by showing that they can be significantly compromised if an attacker
can exercise some control over the input data. In this paper, we revisit this
problem by focusing on behavioral malware clustering approaches, and
investigate whether and to what extent an attacker may be able to subvert these
approaches through a careful injection of samples with poisoning behavior. To
this end, we present a case study on Malheur, an open-source tool for
behavioral malware clustering. Our experiments not only demonstrate that this
tool is vulnerable to poisoning attacks, but also that it can be significantly
compromised even if the attacker can only inject a very small percentage of
attacks into the input data. As a remedy, we discuss possible countermeasures
and highlight the need for more secure clustering algorithms.
|
['Davide Ariu', 'Battista Biggio', 'Igino Corona', 'Giorgio Giacinto', 'Fabio Roli', 'Konrad Rieck', 'Christian Wressnegger']
|
2018-11-25
| null | null | null | null |
['computer-security']
|
['miscellaneous']
|
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|
[5.620843887329102, 7.550308704376221]
|
7d5bde7a-c804-47ba-a62b-444df863108d
|
redeeming-intrinsic-rewards-via-constrained
|
2211.07627
| null |
https://arxiv.org/abs/2211.07627v2
|
https://arxiv.org/pdf/2211.07627v2.pdf
|
Redeeming Intrinsic Rewards via Constrained Optimization
|
State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., $\epsilon$-greedy) for exploration, but this method fails on hard exploration tasks like Montezuma's Revenge. To address the challenge of exploration, prior works incentivize exploration by rewarding the agent when it visits novel states. Such intrinsic rewards (also called exploration bonus or curiosity) often lead to excellent performance on hard exploration tasks. However, on easy exploration tasks, the agent gets distracted by intrinsic rewards and performs unnecessary exploration even when sufficient task (also called extrinsic) reward is available. Consequently, such an overly curious agent performs worse than an agent trained with only task reward. Such inconsistency in performance across tasks prevents the widespread use of intrinsic rewards with RL algorithms. We propose a principled constrained optimization procedure called Extrinsic-Intrinsic Policy Optimization (EIPO) that automatically tunes the importance of the intrinsic reward: it suppresses the intrinsic reward when exploration is unnecessary and increases it when exploration is required. The results is superior exploration that does not require manual tuning in balancing the intrinsic reward against the task reward. Consistent performance gains across sixty-one ATARI games validate our claim. The code is available at https://github.com/Improbable-AI/eipo.
|
['Pulkit Agrawal', 'Joni Pajarinen', 'Zhang-Wei Hong', 'Eric Chen']
|
2022-11-14
| null | null | null | null |
['montezumas-revenge', 'atari-games']
|
['playing-games', 'playing-games']
|
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|
[3.951296329498291, 1.7730259895324707]
|
af744c6d-bff3-42ab-93ba-41a3e398b84b
|
learning-to-deblur
|
1406.7444
| null |
http://arxiv.org/abs/1406.7444v1
|
http://arxiv.org/pdf/1406.7444v1.pdf
|
Learning to Deblur
|
We describe a learning-based approach to blind image deconvolution. It uses a
deep layered architecture, parts of which are borrowed from recent work on
neural network learning, and parts of which incorporate computations that are
specific to image deconvolution. The system is trained end-to-end on a set of
artificially generated training examples, enabling competitive performance in
blind deconvolution, both with respect to quality and runtime.
|
['Bernhard Schölkopf', 'Stefan Harmeling', 'Michael Hirsch', 'Christian J. Schuler']
|
2014-06-28
| null | null | null | null |
['image-deconvolution']
|
['computer-vision']
|
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|
[11.687443733215332, -2.6717329025268555]
|
74f7e5a8-cbde-4a9f-ab02-633820b1c428
|
internal-contrastive-learning-for-generalized
|
2306.15266
| null |
https://arxiv.org/abs/2306.15266v1
|
https://arxiv.org/pdf/2306.15266v1.pdf
|
Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework
|
Fault diagnosis is essential in industrial processes for monitoring the conditions of important machines. With the ever-increasing complexity of working conditions and demand for safety during production and operation, different diagnosis methods are required, and more importantly, an integrated fault diagnosis system that can cope with multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the currently available methods still need improvement for such a generalized system. To address this issue, we propose the Generalized Out-of-distribution Fault Diagnosis (GOOFD) framework to integrate diagnosis subtasks, such as fault detection, fault classification, and novel fault diagnosis. Additionally, a unified fault diagnosis method based on internal contrastive learning is put forward to underpin the proposed generalized framework. The method extracts features utilizing the internal contrastive learning technique and then recognizes the outliers based on the Mahalanobis distance. Experiments are conducted on a simulated benchmark dataset as well as two practical process datasets to evaluate the proposed framework. As demonstrated in the experiments, the proposed method achieves better performance compared with several existing techniques and thus verifies the effectiveness of the proposed framework.
|
['Hongwei Wang', 'Peng Peng', 'Shuting Tao', 'Ke Ma', 'Hanrong Zhang', 'Xingyue Wang']
|
2023-06-27
| null | null | null | null |
['contrastive-learning', 'contrastive-learning', 'fault-detection']
|
['computer-vision', 'methodology', 'miscellaneous']
|
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|
[7.001981735229492, 2.326874256134033]
|
4fdd50ba-546c-4376-b186-cd8e1b4f5cda
|
kiut-knowledge-injected-u-transformer-for-1
|
2306.11345
| null |
https://arxiv.org/abs/2306.11345v1
|
https://arxiv.org/pdf/2306.11345v1.pdf
|
KiUT: Knowledge-injected U-Transformer for Radiology Report Generation
|
Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively distill the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge.
|
['Shaoting Zhang', 'Xiaofan Zhang', 'Zhongzhen Huang']
|
2023-06-20
|
kiut-knowledge-injected-u-transformer-for
|
http://openaccess.thecvf.com//content/CVPR2023/html/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.html
|
http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.pdf
|
cvpr-2023-1
|
['clinical-knowledge']
|
['miscellaneous']
|
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|
[15.038041114807129, -1.422161340713501]
|
aa1d5f9a-6480-443c-a79a-814ce2130494
|
partial-least-square-regression-via-three
|
2208.04324
| null |
https://arxiv.org/abs/2208.04324v2
|
https://arxiv.org/pdf/2208.04324v2.pdf
|
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
|
Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and dependent variables. However, traditional methods to solve PLSR models are usually based on the Euclidean space, and easily getting stuck into a local minimum. To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr). Specifically, we first leverage the three-factor SVD-type decomposition of the cross-covariance matrix defined on the bi-Grassmann manifold, converting the orthogonal constrained optimization problem into an unconstrained optimization problem on bi-Grassmann manifold, and then incorporate the Riemannian preconditioning of matrix scaling to regulate the Riemannian metric in each iteration. PLSRbiGr is validated with a variety of experiments for decoding EEG signals at motor imagery (MI) and steady-state visual evoked potential (SSVEP) task. Experimental results demonstrate that PLSRbiGr outperforms competing algorithms in multiple EEG decoding tasks, which will greatly facilitate small sample data learning.
|
['Quanying Liu', 'JianGuo Zhang', 'Zhichao Liang', 'Wanguang Yin']
|
2022-08-09
| null | null | null | null |
['eeg-decoding', 'eeg-decoding']
|
['medical', 'time-series']
|
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|
[7.922762870788574, 4.438176155090332]
|
764ad0d1-dbd2-43d2-a9ed-fd029c0b3be2
|
panopticfusion-online-volumetric-semantic
|
1903.01177
| null |
https://arxiv.org/abs/1903.01177v2
|
https://arxiv.org/pdf/1903.01177v2.pdf
|
PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things
|
We propose PanopticFusion, a novel online volumetric semantic mapping system at the level of stuff and things. In contrast to previous semantic mapping systems, PanopticFusion is able to densely predict class labels of a background region (stuff) and individually segment arbitrary foreground objects (things). In addition, our system has the capability to reconstruct a large-scale scene and extract a labeled mesh thanks to its use of a spatially hashed volumetric map representation. Our system first predicts pixel-wise panoptic labels (class labels for stuff regions and instance IDs for thing regions) for incoming RGB frames by fusing 2D semantic and instance segmentation outputs. The predicted panoptic labels are integrated into the volumetric map together with depth measurements while keeping the consistency of the instance IDs, which could vary frame to frame, by referring to the 3D map at that moment. In addition, we construct a fully connected conditional random field (CRF) model with respect to panoptic labels for map regularization. For online CRF inference, we propose a novel unary potential approximation and a map division strategy. We evaluated the performance of our system on the ScanNet (v2) dataset. PanopticFusion outperformed or compared with state-of-the-art offline 3D DNN methods in both semantic and instance segmentation benchmarks. Also, we demonstrate a promising augmented reality application using a 3D panoptic map generated by the proposed system.
|
['Takashi Seno', 'Yohsuke Kaji', 'Gaku Narita', 'Tomoya Ishikawa']
|
2019-03-04
| null | null | null | null |
['3d-instance-segmentation-1']
|
['computer-vision']
|
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|
[8.63723373413086, -2.9797134399414062]
|
0509458c-b685-41de-9886-c4be13228ab2
|
ser-fiq-unsupervised-estimation-of-face-image
|
2003.09373
| null |
https://arxiv.org/abs/2003.09373v1
|
https://arxiv.org/pdf/2003.09373v1.pdf
|
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
|
Face image quality is an important factor to enable high performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error-prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems and can be modified to other tasks beyond face recognition.
|
['Naser Damer', 'Philipp Terhörst', 'Arjan Kuijper', 'Jan Niklas Kolf', 'Florian Kirchbuchner']
|
2020-03-20
| null | null | null | null |
['face-quality-assessement', 'robust-face-recognition', 'image-quality-estimation', 'face-image-quality', 'no-reference-image-quality-assessment']
|
['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']
|
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|
[13.102400779724121, 0.8447502851486206]
|
b52f0db7-8eda-4c32-8076-ea216227d371
|
aleda-a-free-large-scale-entity-database-for
| null | null |
https://aclanthology.org/L12-1664
|
https://aclanthology.org/L12-1664.pdf
|
Aleda, a free large-scale entity database for French
|
Named entity recognition, which focuses on the identification of the span and type of named entity mentions in texts, has drawn the attention of the NLP community for a long time. However, many real-life applications need to know which real entity each mention refers to. For such a purpose, often refered to as entity resolution and linking, an inventory of entities is required in order to constitute a reference. In this paper, we describe how we extracted such a resource for French from freely available resources (the French Wikipedia and the GeoNames database). We describe the results of an instrinsic evaluation of the resulting entity database, named Aleda, as well as those of a task-based evaluation in the context of a named entity detection system. We also compare it with the NLGbAse database (Charton and Torres-Moreno, 2010), a resource with similar objectives.
|
['Beno{\\^\\i}t Sagot', 'Rosa Stern']
|
2012-05-01
| null | null | null |
lrec-2012-5
|
['knowledge-base-population']
|
['natural-language-processing']
|
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-5.27580917e-01 8.32922578e-01 8.19945097e-01 1.01080585e+00
3.48801285e-01 2.34401599e-01 1.89960063e-01 -4.90268856e-01
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|
[9.571098327636719, 9.309906959533691]
|
cbaf2e55-e811-432b-8bb3-74fd3386d4ae
|
clip-for-all-things-zero-shot-sketch-based
|
2303.1344
| null |
https://arxiv.org/abs/2303.13440v3
|
https://arxiv.org/pdf/2303.13440v3.pdf
|
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
|
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the category setting and the fine-grained setting ("all"). At the very core of our solution is a prompt learning setup. First we show just via factoring in sketch-specific prompts, we already have a category-level ZS-SBIR system that overshoots all prior arts, by a large margin (24.8%) - a great testimony on studying the CLIP and ZS-SBIR synergy. Moving onto the fine-grained setup is however trickier, and requires a deeper dive into this synergy. For that, we come up with two specific designs to tackle the fine-grained matching nature of the problem: (i) an additional regularisation loss to ensure the relative separation between sketches and photos is uniform across categories, which is not the case for the gold standard standalone triplet loss, and (ii) a clever patch shuffling technique to help establishing instance-level structural correspondences between sketch-photo pairs. With these designs, we again observe significant performance gains in the region of 26.9% over previous state-of-the-art. The take-home message, if any, is the proposed CLIP and prompt learning paradigm carries great promise in tackling other sketch-related tasks (not limited to ZS-SBIR) where data scarcity remains a great challenge. Project page: https://aneeshan95.github.io/Sketch_LVM/
|
['Yi-Zhe Song', 'Tao Xiang', 'Subhadeep Koley', 'Pinaki Nath Chowdhury', 'Ayan Kumar Bhunia', 'Aneeshan Sain']
|
2023-03-23
| null |
http://openaccess.thecvf.com//content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html
|
http://openaccess.thecvf.com//content/CVPR2023/papers/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.pdf
|
cvpr-2023-1
|
['sketch-based-image-retrieval']
|
['computer-vision']
|
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|
[11.619354248046875, 0.6086273193359375]
|
df73a89a-fbf2-4365-822a-b187e2b9f949
|
survival-instinct-in-offline-reinforcement
|
2306.03286
| null |
https://arxiv.org/abs/2306.03286v1
|
https://arxiv.org/pdf/2306.03286v1.pdf
|
Survival Instinct in Offline Reinforcement Learning
|
We present a novel observation about the behavior of offline reinforcement learning (RL) algorithms: on many benchmark datasets, offline RL can produce well-performing and safe policies even when trained with "wrong" reward labels, such as those that are zero everywhere or are negatives of the true rewards. This phenomenon cannot be easily explained by offline RL's return maximization objective. Moreover, it gives offline RL a degree of robustness that is uncharacteristic of its online RL counterparts, which are known to be sensitive to reward design. We demonstrate that this surprising robustness property is attributable to an interplay between the notion of pessimism in offline RL algorithms and a certain bias implicit in common data collection practices. As we prove in this work, pessimism endows the agent with a "survival instinct", i.e., an incentive to stay within the data support in the long term, while the limited and biased data coverage further constrains the set of survival policies. Formally, given a reward class -- which may not even contain the true reward -- we identify conditions on the training data distribution that enable offline RL to learn a near-optimal and safe policy from any reward within the class. We argue that the survival instinct should be taken into account when interpreting results from existing offline RL benchmarks and when creating future ones. Our empirical and theoretical results suggest a new paradigm for RL, whereby an agent is "nudged" to learn a desirable behavior with imperfect reward but purposely biased data coverage.
|
['Ching-An Cheng', 'Andrey Kolobov', 'Dipendra Misra', 'Anqi Li']
|
2023-06-05
| null | null | null | null |
['offline-rl']
|
['playing-games']
|
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-1.46770969e-01 1.18105389e-01 5.58379948e-01 9.33517277e-01
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7.07354724e-01 -6.93004549e-01 -6.75747931e-01 -5.87555707e-01]
|
[4.1658782958984375, 2.393610715866089]
|
5eaaec3a-61a7-4719-ab36-9fdd41c496f2
|
the-analysis-of-synonymy-and-antonymy-in
|
2208.04479
| null |
https://arxiv.org/abs/2208.04479v1
|
https://arxiv.org/pdf/2208.04479v1.pdf
|
The Analysis of Synonymy and Antonymy in Discourse Relations: An interpretable Modeling Approach
|
The idea that discourse relations are construed through explicit content and shared, or implicit, knowledge between producer and interpreter is ubiquitous in discourse research and linguistics. However, the actual contribution of the lexical semantics of arguments is unclear. We propose a computational approach to the analysis of contrast and concession relations in the PDTB corpus. Our work sheds light on the extent to which lexical semantics contributes to signaling explicit and implicit discourse relations and clarifies the contribution of different parts of speech in both. This study contributes to bridging the gap between corpus linguistics and computational linguistics by proposing transparent and explainable models of discourse relations based on the synonymy and antonymy of their arguments.
|
['J. Hermosillo-Valadez', 'A. Taroni', 'M. Toledo-Acosta', 'E. Morales-González', 'D. Torres-Moreno', 'A. Reig-Alamillo']
|
2022-08-09
| null | null | null | null |
['explainable-models']
|
['computer-vision']
|
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-6.63226426e-01 6.65752828e-01 5.05203664e-01 1.55277896e+00
-1.25982434e-01 4.39711809e-01 -1.73219636e-01 7.97847509e-02
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|
[10.691957473754883, 9.465415000915527]
|
687819f5-2c9d-4a39-9490-7ba8cef6f675
|
coadnet-collaborative-aggregation-and
|
2011.04887
| null |
https://arxiv.org/abs/2011.04887v1
|
https://arxiv.org/pdf/2011.04887v1.pdf
|
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
|
Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. In this paper, we present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images. First, we integrate saliency priors into the backbone features to suppress the redundant background information through an online intra-saliency guidance structure. After that, we design a two-stage aggregate-and-distribute architecture to explore group-wise semantic interactions and produce the co-saliency features. In the first stage, we propose a group-attentional semantic aggregation module that models inter-image relationships to generate the group-wise semantic representations. In the second stage, we propose a gated group distribution module that adaptively distributes the learned group semantics to different individuals in a dynamic gating mechanism. Finally, we develop a group consistency preserving decoder tailored for the CoSOD task, which maintains group constraints during feature decoding to predict more consistent full-resolution co-saliency maps. The proposed CoADNet is evaluated on four prevailing CoSOD benchmark datasets, which demonstrates the remarkable performance improvement over ten state-of-the-art competitors.
|
['Yao Zhao', 'Chongyi Li', 'Junhui Hou', 'Runmin Cong', 'Qijian Zhang']
|
2020-11-10
| null |
http://proceedings.neurips.cc/paper/2020/hash/4dc3ed26a29c9c3df3ec373524377a5b-Abstract.html
|
http://proceedings.neurips.cc/paper/2020/file/4dc3ed26a29c9c3df3ec373524377a5b-Paper.pdf
|
neurips-2020-12
|
['co-saliency-detection']
|
['computer-vision']
|
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|
[9.818048477172852, -0.31079959869384766]
|
edd8cf36-e9ad-4a8f-82e7-94ece3486e25
|
semantic-enhanced-differentiable-search-index
|
2305.15115
| null |
https://arxiv.org/abs/2305.15115v1
|
https://arxiv.org/pdf/2305.15115v1.pdf
|
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
|
Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.
|
['Xueqi Cheng', 'Dawei Yin', 'Shuaiqiang Wang', 'Zuowei Zhu', 'Jiangui Chen', 'Jiafeng Guo', 'Ruqing Zhang', 'Yubao Tang']
|
2023-05-24
| null | null | null | null |
['memorization']
|
['natural-language-processing']
|
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|
[11.417061805725098, 7.743794918060303]
|
888614c9-6ebe-4283-9bff-41b6c11766d7
|
leaper-modeling-cloud-fpga-based-systems-via
|
2208.10606
| null |
https://arxiv.org/abs/2208.10606v2
|
https://arxiv.org/pdf/2208.10606v2.pdf
|
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
|
Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design space exploration. First, training requires large amounts of data (features extracted from design synthesis and implementation tools), which is cost-inefficient because of the time-consuming accelerator design and implementation process. Second, a model trained for a specific environment cannot predict performance or resource usage for a new, unknown environment. In a cloud system, renting a platform for data collection to build an ML model can significantly increase the total-cost-ownership (TCO) of a system. Third, ML-based models trained using a limited number of samples are prone to overfitting. To overcome these limitations, we propose LEAPER, a transfer learning-based approach for prediction of performance and resource usage in FPGA-based systems. The key idea of LEAPER is to transfer an ML-based performance and resource usage model trained for a low-end edge environment to a new, high-end cloud environment to provide fast and accurate predictions for accelerator implementation. Experimental results show that LEAPER (1) provides, on average across six workloads and five FPGAs, 85% accuracy when we use our transferred model for prediction in a cloud environment with 5-shot learning and (2) reduces design-space exploration time for accelerator implementation on an FPGA by 10x, from days to only a few hours.
|
['Onur Mutlu', 'Henk Corporaal', 'Sander Stuijk', 'Juan Gómez-Luna', 'Dionysios Diamantopoulos', 'Gagandeep Singh']
|
2022-08-22
| null | null | null | null |
['design-synthesis']
|
['adversarial']
|
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|
[6.048603057861328, 3.4113962650299072]
|
418d0252-5361-4584-859b-d8e91eb9212c
|
gesture-based-arabic-sign-language
|
2203.05602
| null |
https://arxiv.org/abs/2203.05602v1
|
https://arxiv.org/pdf/2203.05602v1.pdf
|
Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network
|
The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states. The Convolution Neural Network has been encapsulated in the proposed system which is based on the machine learning technique. For the recognition of the Arabic Sign Language, the wearable sensor is utilized. This approach has been used a different system that could suit all Arabic gestures. This could be used by the impaired people of the local Arabic community. The research method has been used with reasonable and moderate accuracy. A deep Convolutional network is initially developed for feature extraction from the data gathered by the sensing devices. These sensors can reliably recognize the Arabic sign language's 30 hand sign letters. The hand movements in the dataset were captured using DG5-V hand gloves with wearable sensors. For categorization purposes, the CNN technique is used. The suggested system takes Arabic sign language hand gestures as input and outputs vocalized speech as output. The results were recognized by 90% of the people.
|
['Ahmed I. Taloba', 'Osama R. Shahin', 'Rady El Rwelli']
|
2022-03-10
| null | null | null | null |
['sign-language-recognition']
|
['computer-vision']
|
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|
[9.066426277160645, -6.3672614097595215]
|
b1c9def0-a3ca-4286-9b8a-1eae5fc12f0b
|
dom-lm-learning-generalizable-representations
|
2201.10608
| null |
https://arxiv.org/abs/2201.10608v1
|
https://arxiv.org/pdf/2201.10608v1.pdf
|
DOM-LM: Learning Generalizable Representations for HTML Documents
|
HTML documents are an important medium for disseminating information on the Web for human consumption. An HTML document presents information in multiple text formats including unstructured text, structured key-value pairs, and tables. Effective representation of these documents is essential for machine understanding to enable a wide range of applications, such as Question Answering, Web Search, and Personalization. Existing work has either represented these documents using visual features extracted by rendering them in a browser, which is typically computationally expensive, or has simply treated them as plain text documents, thereby failing to capture useful information presented in their HTML structure. We argue that the text and HTML structure together convey important semantics of the content and therefore warrant a special treatment for their representation learning. In this paper, we introduce a novel representation learning approach for web pages, dubbed DOM-LM, which addresses the limitations of existing approaches by encoding both text and DOM tree structure with a transformer-based encoder and learning generalizable representations for HTML documents via self-supervised pre-training. We evaluate DOM-LM on a variety of webpage understanding tasks, including Attribute Extraction, Open Information Extraction, and Question Answering. Our extensive experiments show that DOM-LM consistently outperforms all baselines designed for these tasks. In particular, DOM-LM demonstrates better generalization performance both in few-shot and zero-shot settings, making it attractive for making it suitable for real-world application settings with limited labeled data.
|
['Huan Sun', 'Binxuan Huang', 'Colin Lockard', 'Prashant Shiralkar', 'Xiang Deng']
|
2022-01-25
| null | null | null | null |
['open-information-extraction']
|
['natural-language-processing']
|
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|
[9.84322452545166, 7.9509429931640625]
|
977ea9f0-1e58-4579-8c00-205f0dfa436b
|
emg-wrist-hand-motion-recognition-system-for
|
1903.06764
| null |
http://arxiv.org/abs/1903.06764v1
|
http://arxiv.org/pdf/1903.06764v1.pdf
|
EMG wrist-hand motion recognition system for real-time Embedded platform
|
Electromyography (EMG) signal analysis is a popular method for controlling
prosthetic and gesture control equipment. For portable systems, such as
prosthetic limbs, real-time low-power operation on embedded processors is
critical, but to date, there has been no record of how existing EMG analysis
approaches support such deployments. This paper presents a novel approach to
time-domain classification of multi-channel EMG signals harnessed from
randomly-placed sensors according to the wrist-hand movements which caused
their occurrence. It shows how, by employing a very small set of time-domain
features, Kernel Fisher discriminant feature projection and Radial Bias
Function neural network classifiers, nine wrist-hand movements can be detected
with accuracy exceeding 99% - surpassing the state-of-the-art on record. It
also shows how, when deployed on ARM Cortex-A53, the processing time is not
only sufficient to enable real-time processing but is also a factor 50 shorter
than the leading time-frequency techniques on record.
|
[]
|
2019-03-15
| null | null | null | null |
['electromyography-emg']
|
['medical']
|
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|
[6.847260475158691, 0.1832403987646103]
|
ee7f2c30-fe0b-4cca-bba9-d72f5b9c35f9
|
constructing-a-visual-relationship
|
2010.05185
| null |
https://arxiv.org/abs/2010.05185v1
|
https://arxiv.org/pdf/2010.05185v1.pdf
|
Constructing a Visual Relationship Authenticity Dataset
|
A visual relationship denotes a relationship between two objects in an image, which can be represented as a triplet of (subject; predicate; object). Visual relationship detection is crucial for scene understanding in images. Existing visual relationship detection datasets only contain true relationships that correctly describe the content in an image. However, distinguishing false visual relationships from true ones is also crucial for image understanding and grounded natural language processing. In this paper, we construct a visual relationship authenticity dataset, where both true and false relationships among all objects appeared in the captions in the Flickr30k entities image caption dataset are annotated. The dataset is available at https://github.com/codecreator2053/VR_ClassifiedDataset. We hope that this dataset can promote the study on both vision and language understanding.
|
['Yuta Nakashima', 'Mishra Vipul', 'Yuto Takebayashi', 'Chenhui Chu']
|
2020-10-11
| null | null | null | null |
['visual-relationship-detection']
|
['computer-vision']
|
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|
[10.368066787719727, 1.6159604787826538]
|
e1520652-8559-467f-bd9d-dc39f147097f
|
network-resource-allocation-strategy-based-on
|
2202.03193
| null |
https://arxiv.org/abs/2202.03193v1
|
https://arxiv.org/pdf/2202.03193v1.pdf
|
Network Resource Allocation Strategy Based on Deep Reinforcement Learning
|
The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.
|
['Peiying Zhang', 'Xinhong You', 'Youxiang Duan', 'Junsan Zhang', 'Chao Wang', 'Shidong Zhang']
|
2022-02-03
| null | null | null | null |
['network-embedding']
|
['methodology']
|
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|
[5.842926502227783, 1.7336089611053467]
|
ccea8d9b-6599-4ac4-a485-c11b923a98b7
|
hgt-a-hierarchical-gcn-based-transformer-for
|
2305.18022
| null |
https://arxiv.org/abs/2305.18022v1
|
https://arxiv.org/pdf/2305.18022v1.pdf
|
HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using CT Images and Text
|
Prosthetic Joint Infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based feature fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4\% and an area under the curve (AUC) of 95.9\%, outperforming recent multimodal approaches by 2.9\% in ACC and 2.2\% in AUC, with a parameter count of only 68M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.
|
['Hongwei Shi', 'Xianjie Liu', 'Fujun Yang', 'Ruiyang Li']
|
2023-05-29
| null | null | null | null |
['computed-tomography-ct']
|
['methodology']
|
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|
[15.028552055358887, -2.049055814743042]
|
c4491e3d-0f7a-44c1-b839-bfddc2032ff5
|
keypoint-graspnet-keypoint-based-6-dof-grasp
|
2209.08752
| null |
https://arxiv.org/abs/2209.08752v4
|
https://arxiv.org/pdf/2209.08752v4.pdf
|
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input
|
Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of the gripper keypoints in the image space and then recover the SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive shape and the grasp family is constructed to examine our idea. Metric-based evaluation reveals that our method outperforms the baselines in terms of the grasp proposal accuracy, diversity, and the time cost. Finally, robot experiments show high success rate, demonstrating the potential of the idea in the real-world applications.
|
['Patricio Vela', 'Ruinian Xu', 'Yunzhi Lin', 'Yiye Chen']
|
2022-09-19
| null | null | null | null |
['grasp-generation']
|
['computer-vision']
|
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|
[5.85009241104126, -0.9018558263778687]
|
00194a02-715d-4838-aa66-59283fba0d8b
|
bi-directional-domain-adaptation-for-sim2real
|
2011.12421
| null |
https://arxiv.org/abs/2011.12421v2
|
https://arxiv.org/pdf/2011.12421v2.pdf
|
Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents
|
Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data. However, no simulation is perfect, and robots trained solely in simulation fail to generalize to the real-world, resulting in a "sim-vs-real gap". How can we overcome the trade-off between the abundance of less accurate, artificial data from simulators and the scarcity of reliable, real-world data? In this paper, we propose Bi-directional Domain Adaptation (BDA), a novel approach to bridge the sim-vs-real gap in both directions -- real2sim to bridge the visual domain gap, and sim2real to bridge the dynamics domain gap. We demonstrate the benefits of BDA on the task of PointGoal Navigation. BDA with only 5k real-world (state, action, next-state) samples matches the performance of a policy fine-tuned with ~600k samples, resulting in a speed-up of ~120x.
|
['Dhruv Batra', 'Sonia Chernova', 'Joanne Truong']
|
2020-11-24
| null | null | null | null |
['pointgoal-navigation']
|
['robots']
|
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|
[4.573051452636719, 1.1913447380065918]
|
1619af34-02d5-442e-8a4c-17f473ce6f7d
|
nonparametric-causal-discovery-with
|
2306.1652
| null |
https://arxiv.org/abs/2306.16520v1
|
https://arxiv.org/pdf/2306.16520v1.pdf
|
Nonparametric causal discovery with applications to cancer bioinformatics
|
Many natural phenomena are intrinsically causal. The discovery of the cause-effect relationships implicit in these processes can help us to understand and describe them more effectively, which boils down to causal discovery about the data and variables that describe them. However, causal discovery is not an easy task. Current methods for this are extremely complex and costly, and their usefulness is strongly compromised in contexts with large amounts of data or where the nature of the variables involved is unknown. As an alternative, this paper presents an original methodology for causal discovery, built on essential aspects of the main theories of causality, in particular probabilistic causality, with many meeting points with the inferential approach of regularity theories and others. Based on this methodology, a non-parametric algorithm is developed for the discovery of causal relationships between binary variables associated to data sets, and the modeling in graphs of the causal networks they describe. This algorithm is applied to gene expression data sets in normal and cancerous prostate tissues, with the aim of discovering cause-effect relationships between gene dysregulations leading to carcinogenesis. The gene characterizations constructed from the causal relationships discovered are compared with another study based on principal component analysis (PCA) on the same data, with satisfactory results.
|
['Jean Pierre Gomez']
|
2023-06-28
| null | null | null | null |
['causal-discovery']
|
['knowledge-base']
|
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|
[7.853438377380371, 5.351152420043945]
|
a728e72c-0025-40a2-a5a6-10d499934105
|
latent-compositional-representations-improve
|
2007.00266
| null |
https://arxiv.org/abs/2007.00266v3
|
https://arxiv.org/pdf/2007.00266v3.pdf
|
Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
|
Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art models in grounded question answering often do not explicitly perform decomposition, leading to difficulties in generalization to out-of-distribution examples. In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. Our model induces latent trees, driven by end-to-end (the answer) supervision only. We show that this inductive bias towards tree structures dramatically improves systematic generalization to out-of-distribution examples, compared to strong baselines on an arithmetic expressions benchmark as well as on CLOSURE, a dataset that focuses on systematic generalization for grounded question answering. On this challenging dataset, our model reaches an accuracy of 96.1%, significantly higher than prior models that almost perfectly solve the task on a random, in-distribution split.
|
['Jonathan Berant', 'Ben Bogin', 'Matt Gardner', 'Sanjay Subramanian']
|
2020-07-01
| null | null | null | null |
['systematic-generalization']
|
['reasoning']
|
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|
[10.767043113708496, 7.885897636413574]
|
2b14ae4f-93ab-425d-9eca-34380a3ce2b3
|
understanding-performance-of-long-document
|
2207.01262
| null |
https://arxiv.org/abs/2207.01262v1
|
https://arxiv.org/pdf/2207.01262v1.pdf
|
Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding
|
We carry out a comprehensive evaluation of 13 recent models for ranking of long documents using two popular collections (MS MARCO documents and Robust04). Our model zoo includes two specialized Transformer models (such as Longformer) that can process long documents without the need to split them. Along the way, we document several difficulties regarding training and comparing such models. Somewhat surprisingly, we find the simple FirstP baseline (truncating documents to satisfy the input-sequence constraint of a typical Transformer model) to be quite effective. We analyze the distribution of relevant passages (inside documents) to explain this phenomenon. We further argue that, despite their widespread use, Robust04 and MS MARCO documents are not particularly useful for benchmarking of long-document models.
|
['Eric Nyberg', 'Jeffrey Huang', 'Yutian Zhao', 'Fangwei Gao', 'Tianyi Lin', 'Leonid Boytsov']
|
2022-07-04
| null | null | null | null |
['document-ranking']
|
['natural-language-processing']
|
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|
[11.564288139343262, 7.7965779304504395]
|
1c500577-d877-4b36-8d9a-05b1e01c9f1c
|
an-efficient-drug-drug-interactions
|
2212.094
| null |
https://arxiv.org/abs/2212.09400v2
|
https://arxiv.org/pdf/2212.09400v2.pdf
|
An Efficient Drug-Drug Interactions Prediction Technology for Molecularly Intelligent Manufacturing
|
Drug-Drug Interactions (DDIs) prediction is an essential issue in the molecular field. Traditional methods of observing DDIs in medical experiments require plenty of resources and labor. In this paper, we present a computational model dubbed MedKGQA based on Graph Neural Networks to automatically predict the DDIs after reading multiple medical documents in the form of multi-hop machine reading comprehension. We introduced a knowledge fusion system to obtain the complete nature of drugs and proteins and exploited a graph reasoning system to infer the drugs and proteins contained in the documents. Our model significantly improves the performance compared to previous state-of-the-art models on the QANGAROO MedHop dataset, which obtained a 4.5% improvement in terms of DDIs prediction accuracy.
|
['Jian-Cheng Ni', 'Feng Gao', 'Peng Gao']
|
2022-12-19
| null | null | null | null |
['machine-reading-comprehension']
|
['natural-language-processing']
|
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|
[5.296639442443848, 5.954298496246338]
|
2139090d-b320-4012-a178-af6f25f43de7
|
reinforcement-learning-for-personalized
|
1908.00286
| null |
https://arxiv.org/abs/1908.00286v1
|
https://arxiv.org/pdf/1908.00286v1.pdf
|
Reinforcement Learning for Personalized Dialogue Management
|
Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard.
|
['Frank van Harmelen', 'Mark Hoogendoorn', 'Floris den Hengst', 'Joost Bosman']
|
2019-08-01
| null | null | null | null |
['product-recommendation', 'dialogue-management']
|
['miscellaneous', 'natural-language-processing']
|
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|
[13.039886474609375, 7.977753639221191]
|
022f2c6e-1ece-4e27-ae46-1e06dd8ba313
|
injecting-relational-structural
|
1806.08009
| null |
http://arxiv.org/abs/1806.08009v1
|
http://arxiv.org/pdf/1806.08009v1.pdf
|
Injecting Relational Structural Representation in Neural Networks for Question Similarity
|
Effectively using full syntactic parsing information in Neural Networks (NNs)
to solve relational tasks, e.g., question similarity, is still an open problem.
In this paper, we propose to inject structural representations in NNs by (i)
learning an SVM model using Tree Kernels (TKs) on relatively few pairs of
questions (few thousands) as gold standard (GS) training data is typically
scarce, (ii) predicting labels on a very large corpus of question pairs, and
(iii) pre-training NNs on such large corpus. The results on Quora and SemEval
question similarity datasets show that NNs trained with our approach can learn
more accurate models, especially after fine tuning on GS.
|
['Alessandro Moschitti', 'Antonio Uva', 'Daniele Bonadiman']
|
2018-06-20
|
injecting-relational-structural-1
|
https://aclanthology.org/P18-2046
|
https://aclanthology.org/P18-2046.pdf
|
acl-2018-7
|
['question-similarity']
|
['natural-language-processing']
|
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|
[11.205436706542969, 8.06191349029541]
|
fe7b7e92-95d4-4947-a2fb-694ae477e817
|
generative-flow-networks-a-markov-chain
|
2307.01422
| null |
https://arxiv.org/abs/2307.01422v1
|
https://arxiv.org/pdf/2307.01422v1.pdf
|
Generative Flow Networks: a Markov Chain Perspective
|
While Markov chain Monte Carlo methods (MCMC) provide a general framework to sample from a probability distribution defined up to normalization, they often suffer from slow convergence to the target distribution when the latter is highly multi-modal. Recently, Generative Flow Networks (GFlowNets) have been proposed as an alternative framework to mitigate this issue when samples have a clear compositional structure, by treating sampling as a sequential decision making problem. Although they were initially introduced from the perspective of flow networks, the recent advances of GFlowNets draw more and more inspiration from the Markov chain literature, bypassing completely the need for flows. In this paper, we formalize this connection and offer a new perspective for GFlowNets using Markov chains, showing a unifying view for GFlowNets regardless of the nature of the state space as recurrent Markov chains. Positioning GFlowNets under the same theoretical framework as MCMC methods also allows us to identify the similarities between both frameworks, and most importantly to highlight their
|
['Yoshua Bengio', 'Tristan Deleu']
|
2023-07-04
| null | null | null | null |
['decision-making']
|
['reasoning']
|
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|
[6.903027534484863, 3.967801094055176]
|
fe21bea9-845e-4934-ab72-b97e4e562a52
|
pricing-football-players-using-neural
|
1711.05865
| null |
http://arxiv.org/abs/1711.05865v2
|
http://arxiv.org/pdf/1711.05865v2.pdf
|
Pricing Football Players using Neural Networks
|
We designed a multilayer perceptron neural network to predict the price of a
football (soccer) player using data on more than 15,000 players from the
football simulation video game FIFA 2017. The network was optimized by
experimenting with different activation functions, number of neurons and
layers, learning rate and its decay, Nesterov momentum based stochastic
gradient descent, L2 regularization, and early stopping. Simultaneous
exploration of various aspects of neural network training is performed and
their trade-offs are investigated. Our final model achieves a top-5 accuracy of
87.2% among 119 pricing categories, and places any footballer within 6.32% of
his actual price on average.
|
['Sourya Dey']
|
2017-11-16
| null | null | null | null |
['l2-regularization', 'game-of-football']
|
['methodology', 'playing-games']
|
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|
[3.498016834259033, 1.4335614442825317]
|
6d64f269-4331-4942-97cb-ecf778b0a2af
|
learning-to-reason-with-relational
|
2210.02615
| null |
https://arxiv.org/abs/2210.02615v2
|
https://arxiv.org/pdf/2210.02615v2.pdf
|
Learning to Reason With Relational Abstractions
|
Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the resulting model-generated sequences may not reflect the kind of systematic reasoning we might expect an expert human to produce. In this paper, we study how to build stronger reasoning capability in language models using the idea of relational abstractions. We introduce new types of sequences that more explicitly provide an abstract characterization of the transitions through intermediate solution steps to the goal state. We find that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy, and models that are trained to produce such sequences solve problems better than those that are trained with previously used human-generated sequences and other baselines. Our work thus takes several steps toward elucidating and improving how language models perform on tasks requiring multi-step mathematical reasoning.
|
['James L. McClelland', 'Chelsea Finn', 'Mengye Ren', 'Andrew J. Nam']
|
2022-10-06
| null | null | null | null |
['mathematical-reasoning']
|
['natural-language-processing']
|
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|
[9.32018756866455, 7.335941314697266]
|
3be03751-4df8-4e3f-9b2f-afeabe1dd28c
|
stl-cqa-structure-based-transformers-with
| null | null |
https://aclanthology.org/2020.emnlp-main.264
|
https://aclanthology.org/2020.emnlp-main.264.pdf
|
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering
|
Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach. We conduct extensive experiments while proposing pre-training tasks, methodology and also an improved dataset with more complex and balanced questions of different types. The proposed methodology shows a significant accuracy improvement compared to the state-of-the-art approaches on various chart Q/A datasets, while outperforming even human baseline on the DVQA Dataset. We also demonstrate interpretability while examining different components in the inference pipeline.
|
['Sumit Shekhar', 'Hrituraj Singh']
| null | null | null | null |
emnlp-2020-11
|
['chart-question-answering', 'chart-question-answering']
|
['computer-code', 'computer-vision']
|
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|
[11.145679473876953, 2.0340678691864014]
|
ea4a96c4-36a3-4231-a4d1-e01e2247c379
|
longer-version-for-deep-context-encoding
|
2105.14538
| null |
https://arxiv.org/abs/2105.14538v1
|
https://arxiv.org/pdf/2105.14538v1.pdf
|
Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"
|
Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, we propose a new context-driven encoding network to automatically generate medical reports for retinal images. The proposed model is mainly composed of a multi-modal input encoder and a fused-feature decoder. Our experimental results show that our proposed method is capable of effectively leveraging the interactive information between the input image and context, i.e., keywords in our case. The proposed method creates more accurate and meaningful reports for retinal images than baseline models and achieves state-of-the-art performance. This performance is shown in several commonly used metrics for the medical report generation task: BLEU-avg (+16%), CIDEr (+10.2%), and ROUGE (+8.6%).
|
['Marcel Worring', 'Chao-Han Huck Yang', 'Ting-Wei Wu', 'Jia-Hong Huang']
|
2021-05-30
| null | null | null | null |
['medical-report-generation']
|
['medical']
|
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|
[15.05052375793457, -1.3886631727218628]
|
ced60550-9f5b-4e24-bf65-d309d62737c7
|
image-enhancement-for-remote
|
2303.09336
| null |
https://arxiv.org/abs/2303.09336v1
|
https://arxiv.org/pdf/2303.09336v1.pdf
|
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment
|
With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we collect a large-scale dataset that was designed for remote heart rate estimation recorded with various illumination variations to evaluate the performance of the rPPG algorithm (Green, ICA, and POS). We also propose a low-light enhancement solution (technical solution) for remote heart rate estimation under the low-light condition. Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment. The proposed solution for rPPG process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal.
|
['Jianhua Wang', 'Xingming Wu', 'Changchen Zhao', 'Weihai Chen', 'Lin Xi']
|
2023-03-16
| null | null | null | null |
['face-detection', 'image-enhancement', 'heart-rate-estimation']
|
['computer-vision', 'computer-vision', 'medical']
|
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1.92974046e-01 -3.89454991e-01 -6.42670035e-01 1.94063947e-01]
|
[13.873282432556152, 2.8006670475006104]
|
a2729b3a-c54e-40c9-8075-5ef08e9ee9dc
|
avformer-injecting-vision-into-frozen-speech
|
2303.16501
| null |
https://arxiv.org/abs/2303.16501v1
|
https://arxiv.org/pdf/2303.16501v1.pdf
|
AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR
|
Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively; and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.
|
['Cordelia Schmid', 'Arsha Nagrani', 'Paul Hongsuck Seo']
|
2023-03-29
| null |
http://openaccess.thecvf.com//content/CVPR2023/html/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.html
|
http://openaccess.thecvf.com//content/CVPR2023/papers/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.pdf
|
cvpr-2023-1
|
['robust-speech-recognition']
|
['speech']
|
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|
[14.370651245117188, 5.082764148712158]
|
462047da-3fb7-4309-8009-296e8ed840b3
|
blind-image-deconvolution-using-variational
|
2202.00179
| null |
https://arxiv.org/abs/2202.00179v3
|
https://arxiv.org/pdf/2202.00179v3.pdf
|
Blind Image Deconvolution Using Variational Deep Image Prior
|
Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well to blurs unseen in the training dataset. Thus, training image-specific models is important for higher generalization. Deep image prior (DIP) provides an approach to optimize the weights of a randomly initialized network with a single degraded image by maximum a posteriori (MAP), which shows that the architecture of a network can serve as the hand-crafted image prior. Different from the conventional hand-crafted image priors that are statistically obtained, it is hard to find a proper network architecture because the relationship between images and their corresponding network architectures is unclear. As a result, the network architecture cannot provide enough constraint for the latent sharp image. This paper proposes a new variational deep image prior (VDIP) for blind image deconvolution, which exploits additive hand-crafted image priors on latent sharp images and approximates a distribution for each pixel to avoid suboptimal solutions. Our mathematical analysis shows that the proposed method can better constrain the optimization. The experimental results further demonstrate that the generated images have better quality than that of the original DIP on benchmark datasets. The source code of our VDIP is available at https://github.com/Dong-Huo/VDIP-Deconvolution.
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['Yee-Hong Yang', 'Rafsanjany Kushol', 'Abbas Masoumzadeh', 'Dong Huo']
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2022-02-01
| null | null | null | null |
['image-deconvolution']
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['computer-vision']
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|
[11.592201232910156, -2.6627037525177]
|
be9aa421-0b3a-4922-98b5-b95e24ca2349
|
tess-text-to-text-self-conditioned-simplex
|
2305.08379
| null |
https://arxiv.org/abs/2305.08379v1
|
https://arxiv.org/pdf/2305.08379v1.pdf
|
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
|
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work, we propose Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully non-autoregressive, employs a new form of self-conditioning, and applies the diffusion process on the logit simplex space rather than the typical learned embedding space. Through extensive experiments on natural language understanding and generation tasks including summarization, text simplification, paraphrase generation, and question generation, we demonstrate that TESS outperforms state-of-the-art non-autoregressive models and is competitive with pretrained autoregressive sequence-to-sequence models.
|
['Arman Cohan', 'Matthew E. Peters', 'Iz Beltagy', 'James Henderson', 'Hamish Ivison', 'Jaesung Tae', 'Rabeeh Karimi Mahabadi']
|
2023-05-15
| null | null | null | null |
['paraphrase-generation', 'question-generation', 'paraphrase-generation']
|
['computer-code', 'natural-language-processing', 'natural-language-processing']
|
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|
[11.889513969421387, 9.123397827148438]
|
9821f863-cd5e-4521-9014-6118f5188c1c
|
greek-sign-language-recognition-for-the-sl
| null | null |
https://aclanthology.org/2022.sltat-1.12
|
https://aclanthology.org/2022.sltat-1.12.pdf
|
Greek Sign Language Recognition for the SL-ReDu Learning Platform
|
There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners’ SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign Language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively.
|
['Petros Maragos', 'Stavroula-Evita Fotinea', 'Eleni Efthimiou', 'Theodore Goulas', 'Galini Sapountzaki', 'Gerasimos Potamianos', 'Katerina Papadimitriou']
| null | null | null | null |
sltat-lrec-2022-6
|
['sign-language-recognition']
|
['computer-vision']
|
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|
[9.121271133422852, -6.437844276428223]
|
88f2d147-e520-4b06-9d2d-553db999388d
|
training-time-adversarial-attack-aiming-the
|
2211.15875
| null |
https://arxiv.org/abs/2211.15875v2
|
https://arxiv.org/pdf/2211.15875v2.pdf
|
Data Poisoning Attack Aiming the Vulnerability of Continual Learning
|
Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this introduces a problem in these models by not being able to track the performance on each task. In essence, current continual learning methods are susceptible to attacks on previous tasks. We demonstrate the vulnerability of regularization-based continual learning methods by presenting a simple task-specific data poisoning attack that can be used in the learning process of a new task. Training data generated by the proposed attack causes performance degradation on a specific task targeted by the attacker. We experiment with the attack on the two representative regularization-based continual learning methods, Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI), trained with variants of MNIST dataset. The experiment results justify the vulnerability proposed in this paper and demonstrate the importance of developing continual learning models that are robust to adversarial attacks.
|
['Junmo Kim', 'Hyeong Gwon Hong', 'Jaehyun Choi', 'Gyojin Han']
|
2022-11-29
| null | null | null | null |
['data-poisoning']
|
['adversarial']
|
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|
[5.795494556427002, 7.626213550567627]
|
2207f660-467b-44b5-b028-954f62b7edf3
|
voxceleb-enrichment-for-age-and-gender
|
2109.1351
| null |
https://arxiv.org/abs/2109.13510v2
|
https://arxiv.org/pdf/2109.13510v2.pdf
|
VoxCeleb Enrichment for Age and Gender Recognition
|
VoxCeleb datasets are widely used in speaker recognition studies. Our work serves two purposes. First, we provide speaker age labels and (an alternative) annotation of speaker gender. Second, we demonstrate the use of this metadata by constructing age and gender recognition models with different features and classifiers. We query different celebrity databases and apply consensus rules to derive age and gender labels. We also compare the original VoxCeleb gender labels with our labels to identify records that might be mislabeled in the original VoxCeleb data. On modeling side, we design a comprehensive study of multiple features and models for recognizing gender and age. Our best system, using i-vector features, achieved an F1-score of 0.9829 for gender recognition task using logistic regression, and the lowest mean absolute error (MAE) in age regression, 9.443 years, is obtained with ridge regression. This indicates challenge in age estimation from in-the-wild style speech data.
|
['Tomi Kinnunen', 'Ville Hautamaki', 'Trung Ngo Trong', 'Khaled Hechmi']
|
2021-09-28
| null | null | null | null |
['age-estimation', 'age-estimation']
|
['computer-vision', 'miscellaneous']
|
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|
[14.161341667175293, 6.115533351898193]
|
02fa5b16-fb15-47b6-8d12-23bdcae2f180
|
variational-bayesian-filtering-with-subspace
|
2201.08307
| null |
https://arxiv.org/abs/2201.08307v1
|
https://arxiv.org/pdf/2201.08307v1.pdf
|
Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion
|
Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sampling and noisy sampling, a case present in many real-world problems in the field of traffic sensing and environment monitoring, etc. However, jointly exploiting the spatiotemporal and periodic structure, which is generally not captured by classical matrix completion approaches, can improve the imputation performance of sensor data in such real-world conditions. We present a Bayesian approach towards spatiotemporal matrix completion wherein we estimate the underlying temporarily varying subspace using a Variational Bayesian technique. We jointly couple the low-rank matrix completion with the state space autoregressive framework along with a penalty function on the slowly varying subspace to model the temporal and periodic evolution in the data. A major advantage of our method is that a critical parameter like the rank of the model is automatically tuned using the automatic relevance determination (ARD) approach, unlike most matrix/tensor completion techniques. We also propose a robust version of the above formulation, which improves the performance of imputation in the presence of outliers. We evaluate the proposed Variational Bayesian Filtering with Subspace Information (VBFSI) method to impute matrices in real-world traffic and air pollution data. Simulation results demonstrate that the proposed method outperforms the recent state-of-the-art methods and provides a sufficiently accurate imputation for different sampling rates. In particular, we demonstrate that fusing the subspace evolution over days can improve the imputation performance with even 15% of the data sampling.
|
['Ketan Rajawat', 'Pravesh Biyani', 'Charul Paliwal']
|
2022-01-20
| null | null | null | null |
['low-rank-matrix-completion']
|
['methodology']
|
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|
[6.599430084228516, 2.2086541652679443]
|
c5846cfd-49cd-4275-8ef6-f4b71a421ef2
|
the-role-of-context-in-neural-morphological
| null | null |
https://aclanthology.org/C16-1018
|
https://aclanthology.org/C16-1018.pdf
|
The Role of Context in Neural Morphological Disambiguation
|
Languages with rich morphology often introduce sparsity in language processing tasks. While morphological analyzers can reduce this sparsity by providing morpheme-level analyses for words, they will often introduce ambiguity by returning multiple analyses for the same surface form. The problem of disambiguating between these morphological parses is further complicated by the fact that a correct parse for a word is not only be dependent on the surface form but also on other words in its context. In this paper, we present a language-agnostic approach to morphological disambiguation. We address the problem of using context in morphological disambiguation by presenting several LSTM-based neural architectures that encode long-range surface-level and analysis-level contextual dependencies. We applied our approach to Turkish, Russian, and Arabic to compare effectiveness across languages, matching state-of-the-art results in two of the three languages. Our results also demonstrate that while context plays a role in learning how to disambiguate, the type and amount of context needed varies between languages.
|
['Daniel Clothiaux', 'Qinlan Shen', 'Patrick Littell', 'Emily Tagtow', 'Chris Dyer']
|
2016-12-01
|
the-role-of-context-in-neural-morphological-1
|
https://aclanthology.org/C16-1018
|
https://aclanthology.org/C16-1018.pdf
|
coling-2016-12
|
['morphological-disambiguation']
|
['natural-language-processing']
|
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|
[10.415495872497559, 10.03642463684082]
|
32459846-ce05-4fd7-82dd-a82c2840564f
|
irgan-a-minimax-game-for-unifying-generative
|
1705.10513
| null |
http://arxiv.org/abs/1705.10513v2
|
http://arxiv.org/pdf/1705.10513v2.pdf
|
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
|
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.
|
['Wei-Nan Zhang', 'Lantao Yu', 'Yinghui Xu', 'Dell Zhang', 'Benyou Wang', 'Jun Wang', 'Yu Gong', 'Peng Zhang']
|
2017-05-30
| null | null | null | null |
['ad-hoc-information-retrieval']
|
['natural-language-processing']
|
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|
[11.491297721862793, 7.57740592956543]
|
fa2dd3f2-253d-44ad-82fd-435314feb4b9
|
recurrent-models-for-auditory-attention-in
|
1511.06407
| null |
http://arxiv.org/abs/1511.06407v2
|
http://arxiv.org/pdf/1511.06407v2.pdf
|
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition
|
Integration of multiple microphone data is one of the key ways to achieve
robust speech recognition in noisy environments or when the speaker is located
at some distance from the input device. Signal processing techniques such as
beamforming are widely used to extract a speech signal of interest from
background noise. These techniques, however, are highly dependent on prior
spatial information about the microphones and the environment in which the
system is being used. In this work, we present a neural attention network that
directly combines multi-channel audio to generate phonetic states without
requiring any prior knowledge of the microphone layout or any explicit signal
preprocessing for speech enhancement. We embed an attention mechanism within a
Recurrent Neural Network (RNN) based acoustic model to automatically tune its
attention to a more reliable input source. Unlike traditional multi-channel
preprocessing, our system can be optimized towards the desired output in one
step. Although attention-based models have recently achieved impressive results
on sequence-to-sequence learning, no attention mechanisms have previously been
applied to learn potentially asynchronous and non-stationary multiple inputs.
We evaluate our neural attention model on the CHiME-3 challenge task, and show
that the model achieves comparable performance to beamforming using a purely
data-driven method.
|
['Suyoun Kim', 'Ian Lane']
|
2015-11-19
| null | null | null | null |
['robust-speech-recognition']
|
['speech']
|
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|
[14.914254188537598, 5.981829643249512]
|
b853bd23-929d-426a-863a-f8b0e77720f4
|
image-and-texture-independent-deep-learning
|
2207.07604
| null |
https://arxiv.org/abs/2207.07604v1
|
https://arxiv.org/pdf/2207.07604v1.pdf
|
Image and Texture Independent Deep Learning Noise Estimation using Multiple Frames
|
In this study, a novel multiple-frame based image and texture independent convolutional Neural Network (CNN) noise estimator is introduced. The estimator works.
|
['Nurettin Besli', 'Hikmet Kirmizitas']
|
2022-07-15
| null | null | null | null |
['noise-estimation']
|
['medical']
|
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|
[11.459898948669434, -2.3086636066436768]
|
23e259dd-f627-421c-bc0c-341d088fb39c
|
transalign-fully-automatic-and-effective
|
2210.0854
| null |
https://arxiv.org/abs/2210.08540v1
|
https://arxiv.org/pdf/2210.08540v1.pdf
|
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
|
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this paper, we propose the first fully automatic alignment method named TransAlign, which does not require any manually crafted seed alignments. Specifically, for predicate embeddings, TransAlign constructs a predicate-proximity-graph to automatically capture the similarity between predicates across two KGs by learning the attention of entity types. For entity embeddings, TransAlign first computes the entity embeddings of each KG independently using TransE, and then shifts the two KGs' entity embeddings into the same vector space by computing the similarity between entities based on their attributes. Thus, both predicate alignment and entity alignment can be done without manually crafted seed alignments. TransAlign is not only fully automatic, but also highly effective. Experiments using real-world KGs show that TransAlign improves the accuracy of entity alignment significantly compared to state-of-the-art methods.
|
['Jianzhong Qi', 'Hong Cheng', 'Min Yang', 'Bayu Distiawan Trisedya', 'Xiaoyan Zhao', 'Rui Zhang']
|
2022-10-16
| null | null | null | null |
['entity-alignment', 'entity-embeddings', 'entity-alignment']
|
['knowledge-base', 'methodology', 'natural-language-processing']
|
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|
[8.73729419708252, 7.962116241455078]
|
8aaf2790-36fb-4971-bb5f-1fb8c15bef28
|
large-scale-autonomous-driving-scenarios
|
2103.16101
| null |
https://arxiv.org/abs/2103.16101v1
|
https://arxiv.org/pdf/2103.16101v1.pdf
|
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction
|
The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity. This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Existing algorithms utilize handcrafted features whose quality relies on the judgments of human experts. Additionally, the related feature compression methods are not scalable for a large data-set. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. Meanwhile, we proposed a self-supervised deep learning approach for spatial and temporal feature extraction to avoid biased data representation. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled data-set, which is subject to human bias. Via such unprejudiced evaluation metrics, we have shown our approach surpasses the existing methods that rely on handcrafted feature extractions.
|
['Liangjun Zhang', 'Zhixian Ye', 'Jin Fang', 'Jinxin Zhao']
|
2021-03-30
| null | null | null | null |
['feature-compression']
|
['computer-vision']
|
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|
[8.054669380187988, -1.4387166500091553]
|
32175bcb-480e-4134-94dc-9602984acb7d
|
factual-consistency-of-multilingual
|
2203.11552
| null |
https://arxiv.org/abs/2203.11552v1
|
https://arxiv.org/pdf/2203.11552v1.pdf
|
Factual Consistency of Multilingual Pretrained Language Models
|
Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-the-blank differently for paraphrases describing the same fact. In this paper, we extend the analysis of consistency to a multilingual setting. We introduce a resource, mParaRel, and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts; and (ii) if such models are equally consistent across languages. We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages.
|
['Anders Søgaard', 'Constanza Fierro']
|
2022-03-22
| null |
https://aclanthology.org/2022.findings-acl.240
|
https://aclanthology.org/2022.findings-acl.240.pdf
|
findings-acl-2022-5
|
['xlm-r']
|
['natural-language-processing']
|
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|
[9.968100547790527, 8.312031745910645]
|
1cd426f9-311a-4fa3-b444-677923ade194
|
unsupervised-deep-feature-extraction-for
|
1511.08131
| null |
http://arxiv.org/abs/1511.08131v1
|
http://arxiv.org/pdf/1511.08131v1.pdf
|
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
|
This paper introduces the use of single layer and deep convolutional networks
for remote sensing data analysis. Direct application to multi- and
hyper-spectral imagery of supervised (shallow or deep) convolutional networks
is very challenging given the high input data dimensionality and the relatively
small amount of available labeled data. Therefore, we propose the use of greedy
layer-wise unsupervised pre-training coupled with a highly efficient algorithm
for unsupervised learning of sparse features. The algorithm is rooted on sparse
representations and enforces both population and lifetime sparsity of the
extracted features, simultaneously. We successfully illustrate the expressive
power of the extracted representations in several scenarios: classification of
aerial scenes, as well as land-use classification in very high resolution
(VHR), or land-cover classification from multi- and hyper-spectral images. The
proposed algorithm clearly outperforms standard Principal Component Analysis
(PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art
algorithms of aerial classification, while being extremely computationally
efficient at learning representations of data. Results show that single layer
convolutional networks can extract powerful discriminative features only when
the receptive field accounts for neighboring pixels, and are preferred when the
classification requires high resolution and detailed results. However, deep
architectures significantly outperform single layers variants, capturing
increasing levels of abstraction and complexity throughout the feature
hierarchy.
|
['Gustau Camps-Valls', 'Carlo Gatta', 'Adriana Romero']
|
2015-11-25
| null | null | null | null |
['remote-sensing-image-classification']
|
['miscellaneous']
|
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|
[9.68156623840332, -1.478415846824646]
|
0aee43fc-b63c-454a-80d6-b27e515f3a9f
|
group-contextualization-for-video-recognition
|
2203.09694
| null |
https://arxiv.org/abs/2203.09694v1
|
https://arxiv.org/pdf/2203.09694v1.pdf
|
Group Contextualization for Video Recognition
|
Learning discriminative representation from the complex spatio-temporal dynamic space is essential for video recognition. On top of those stylized spatio-temporal computational units, further refining the learnt feature with axial contexts is demonstrated to be promising in achieving this goal. However, previous works generally focus on utilizing a single kind of contexts to calibrate entire feature channels and could hardly apply to deal with diverse video activities. The problem can be tackled by using pair-wise spatio-temporal attentions to recompute feature response with cross-axis contexts at the expense of heavy computations. In this paper, we propose an efficient feature refinement method that decomposes the feature channels into several groups and separately refines them with different axial contexts in parallel. We refer this lightweight feature calibration as group contextualization (GC). Specifically, we design a family of efficient element-wise calibrators, i.e., ECal-G/S/T/L, where their axial contexts are information dynamics aggregated from other axes either globally or locally, to contextualize feature channel groups. The GC module can be densely plugged into each residual layer of the off-the-shelf video networks. With little computational overhead, consistent improvement is observed when plugging in GC on different networks. By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities. On videos with rich temporal variations, empirically GC can boost the performance of 2D-CNN (e.g., TSN and TSM) to a level comparable to the state-of-the-art video networks. Code is available at https://github.com/haoyanbin918/Group-Contextualization.
|
['Xiangnan He', 'Chong-Wah Ngo', 'Hao Zhang', 'Yanbin Hao']
|
2022-03-18
| null |
http://openaccess.thecvf.com//content/CVPR2022/html/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.html
|
http://openaccess.thecvf.com//content/CVPR2022/papers/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.pdf
|
cvpr-2022-1
|
['egocentric-activity-recognition']
|
['computer-vision']
|
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|
[8.993006706237793, 0.4429289400577545]
|
118a75e5-cba8-41dc-b275-45c99b77dbfb
|
relative-geometry-aware-siamese-neural
|
1901.01049
| null |
http://arxiv.org/abs/1901.01049v2
|
http://arxiv.org/pdf/1901.01049v2.pdf
|
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
|
6DOF camera relocalization is an important component of autonomous driving
and navigation. Deep learning has recently emerged as a promising technique to
tackle this problem. In this paper, we present a novel relative geometry-aware
Siamese neural network to enhance the performance of deep learning-based
methods through explicitly exploiting the relative geometry constraints between
images. We perform multi-task learning and predict the absolute and relative
poses simultaneously. We regularize the shared-weight twin networks in both the
pose and feature domains to ensure that the estimated poses are globally as
well as locally correct. We employ metric learning and design a novel adaptive
metric distance loss to learn a feature that is capable of distinguishing poses
of visually similar images from different locations. We evaluate the proposed
method on public indoor and outdoor benchmarks and the experimental results
demonstrate that our method can significantly improve localization performance.
Furthermore, extensive ablation evaluations are conducted to demonstrate the
effectiveness of different terms of the loss function.
|
['Ke Sun', 'Rui Cao', 'Bozhi Liu', 'Qingquan Li', 'Guoping Qiu', 'Qing Li', 'Jonathan M. Garibaldi', 'Jiasong Zhu']
|
2019-01-04
| null | null | null | null |
['camera-relocalization']
|
['computer-vision']
|
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|
[7.644552707672119, -2.090426206588745]
|
10bc7abe-9786-4a77-bc75-87bfb7545260
|
assurance-monitoring-of-learning-enabled
|
2110.0312
| null |
https://arxiv.org/abs/2110.03120v1
|
https://arxiv.org/pdf/2110.03120v1.pdf
|
Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning
|
Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Further, the method is computationally efficient and allows real-time assurance monitoring of CPS.
|
['Xenofon Koutsoukos', 'Dimitrios Boursinos']
|
2021-10-07
| null | null | null | null |
['traffic-sign-recognition']
|
['computer-vision']
|
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|
[5.480321407318115, 7.401754379272461]
|
2b4b1217-83c1-47f6-bfff-51c218ef8231
|
action-priors-for-large-action-spaces-in
|
2101.04178
| null |
https://arxiv.org/abs/2101.04178v2
|
https://arxiv.org/pdf/2101.04178v2.pdf
|
Action Priors for Large Action Spaces in Robotics
|
In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert demonstrations can be expensive. This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks. The action prior is a probability distribution over actions that summarizes the set of policies found solving previous tasks. Our results indicate that this approach can be used to solve robotic manipulation problems that would otherwise be infeasible without expert demonstrations. Source code is available at \url{https://github.com/ondrejba/action_priors}.
|
['Lawson L. S. Wong', 'Jan-Willem van de Meent', 'Robert Platt', 'Dian Wang', 'Ondrej Biza']
|
2021-01-11
| null | null | null | null |
['transfer-reinforcement-learning']
|
['methodology']
|
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|
[4.286700248718262, 1.4351084232330322]
|
995cc098-eccb-422c-8b35-927f9e298bd1
|
the-geometry-of-deep-generative-image-models
|
2101.06006
| null |
https://arxiv.org/abs/2101.06006v2
|
https://arxiv.org/pdf/2101.06006v2.pdf
|
The Geometry of Deep Generative Image Models and its Applications
|
Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs.
|
['Carlos R. Ponce', 'Binxu Wang']
|
2021-01-15
| null | null | null | null |
['image-variation']
|
['computer-vision']
|
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|
[11.749839782714844, -0.0346292182803154]
|
725ce130-9309-44fc-aab2-c227629344e6
|
planes-vs-chairs-category-guided-3d-shape
|
2204.10235
| null |
https://arxiv.org/abs/2204.10235v1
|
https://arxiv.org/pdf/2204.10235v1.pdf
|
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
|
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images of multiple object categories without viewpoint annotation, forcing the model to learn across multiple object categories without 3D supervision. To facilitate learning with such minimal supervision, we use category labels to guide shape learning with a novel categorical metric learning approach. We also utilize adversarial and viewpoint regularization techniques to further disentangle the effects of viewpoint and shape. We obtain the first results for large-scale (more than 50 categories) single-viewpoint shape prediction using a single model without any 3D cues. We are also the first to examine and quantify the benefit of class information in single-view supervised 3D shape reconstruction. Our method achieves superior performance over state-of-the-art methods on ShapeNet-13, ShapeNet-55 and Pascal3D+.
|
['James M. Rehg', 'Varun Jampani', 'Anh Thai', 'Stefan Stojanov', 'Zixuan Huang']
|
2022-04-21
| null | null | null | null |
['3d-shape-representation']
|
['computer-vision']
|
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|
[8.3859281539917, -3.263258218765259]
|
2fa8c6ae-8237-49ce-a07b-bc023b067954
|
hypergraph-pre-training-with-graph-neural
|
2105.10862
| null |
https://arxiv.org/abs/2105.10862v1
|
https://arxiv.org/pdf/2105.10862v1.pdf
|
Hypergraph Pre-training with Graph Neural Networks
|
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting. Among others, a major hurdle for effective hypergraph representation learning lies in the label scarcity of nodes and/or hyperedges. To address this issue, this paper presents an end-to-end, bi-level pre-training strategy with Graph Neural Networks for hypergraphs. The proposed framework named HyperGene bears three distinctive advantages. First, it is capable of ingesting the labeling information when available, but more importantly, it is mainly designed in the self-supervised fashion which significantly broadens its applicability. Second, at the heart of the proposed HyperGene are two carefully designed pretexts, one on the node level and the other on the hyperedge level, which enable us to encode both the local and the global context in a mutually complementary way. Third, the proposed framework can work in both transductive and inductive settings. When applying the two proposed pretexts in tandem, it can accelerate the adaptation of the knowledge from the pre-trained model to downstream applications in the transductive setting, thanks to the bi-level nature of the proposed method. The extensive experimental results demonstrate that: (1) HyperGene achieves up to 5.69% improvements in hyperedge classification, and (2) improves pre-training efficiency by up to 42.80% on average.
|
['Hanghang Tong', 'Tal Neiman', 'Robert Barton', 'Changhe Yuan', 'Boxin Du']
|
2021-05-23
| null | null | null | null |
['hyperedge-classification']
|
['graphs']
|
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|
[7.362224102020264, 6.283175468444824]
|
39c1771d-f615-4952-bf87-f5f82ff3d3b4
|
model-agnostic-high-dimensional-error-in
|
1902.1092
| null |
https://arxiv.org/abs/1902.10920v9
|
https://arxiv.org/pdf/1902.10920v9.pdf
|
On Robustness of Principal Component Regression
|
Principal Component Regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued covariates is not understood and remains an important open challenge. As the main contribution of this work we establish the robustness of PCR in this respect and provide meaningful finite-sample analysis. In the process, we establish that PCR is equivalent to performing Linear Regression after pre-processing the covariate matrix via Hard Singular Value Thresholding (HSVT). That is, PCR is equivalent to the recently proposed robust variant of the Synthetic Control method in the context of counterfactual analysis using observational data. As an immediate consequence, we obtain finite-sample analysis of the Robust Synthetic Control (RSC) estimator that was previously absent. As an important contribution to the Synthetic Control literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model; traditionally, the existence of a synthetic control needs to be assumed to exist as an axiom. We further discuss a surprising implication of the robustness property of PCR with respect to noise, i.e., PCR can learn a good predictive model even if the covariates are tactfully transformed to preserve differential privacy. Finally, this work advances the state-of-the-art analysis for HSVT by establishing stronger guarantees with respect to the $\ell_{2, \infty}$-norm rather than the Frobenius norm as is commonly done in the matrix estimation literature, which may be of interest in its own right.
|
['Dennis Shen', 'Dogyoon Song', 'Anish Agarwal', 'Devavrat Shah']
|
2019-02-28
|
on-robustness-of-principal-component
|
http://papers.nips.cc/paper/9181-on-robustness-of-principal-component-regression
|
http://papers.nips.cc/paper/9181-on-robustness-of-principal-component-regression.pdf
|
neurips-2019-12
|
['art-analysis']
|
['computer-vision']
|
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|
[7.088939666748047, 4.523853302001953]
|
7d1891e4-6203-45ed-b33a-5a3b27707869
|
a-unified-framework-for-multi-sensor-hdr
|
1308.4908
| null |
http://arxiv.org/abs/1308.4908v1
|
http://arxiv.org/pdf/1308.4908v1.pdf
|
A Unified Framework for Multi-Sensor HDR Video Reconstruction
|
One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and allows reconstruction to an
arbitrary resolution and output mapping. We present an implementation in CUDA
and show real-time performance for an experimental 4 Mpixel multi-sensor HDR
video system. We further show that our algorithm has clear advantages over
existing methods, both in terms of flexibility and reconstruction quality.
|
['Anders Ynnerman', 'Stefan Gustavson', 'Gerhard Bonnet', 'Jonas Unger', 'Joel Kronander']
|
2013-08-22
| null | null | null | null |
['video-reconstruction', 'hdr-reconstruction']
|
['computer-vision', 'computer-vision']
|
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|
[10.488283157348633, -2.269416570663452]
|
a923fa99-298a-4e08-8e86-651697de07bb
|
millimeter-wave-wireless-communication
|
2303.02617
| null |
https://arxiv.org/abs/2303.02617v1
|
https://arxiv.org/pdf/2303.02617v1.pdf
|
Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping
|
In this paper, we study the three-dimensional (3D) simultaneous localization and mapping (SLAM) problem in complex outdoor and indoor environments based only on millimeter-wave (mmWave) wireless communication signals. Firstly, we propose a deep-learning based mapping (DLM) algorithm that can leverage the reflections point on the first-order none line-of-sight (NLOS) communications links (CLs) to build the 3D point cloud map of the environment. Specifically, we design a classification neural network to identify the first-order NLOS CL and theoretically calculate the geometric coordinates of the reflection points on it. Secondly, we take the advantage of both the inertial measurement unit and the beam-squint assisted localization method to realize real-time and precise localizations. Then, combining the DLM and the adopted localization algorithm, we develop the communication-based SLAM (C-SLAM) framework that can carry out SLAM without any prior knowledge of the environment. Moreover, extensive simulations of both complex outdoor and indoor environments validate the effectiveness of our approach.
|
['Feifei Gao', 'Zhiyu Mou']
|
2023-03-05
| null | null | null | null |
['simultaneous-localization-and-mapping']
|
['computer-vision']
|
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|
[6.273915767669678, 1.0735268592834473]
|
071832cb-905d-4eae-850e-7df2974b94eb
|
llama-adapter-v2-parameter-efficient-visual
|
2304.1501
| null |
https://arxiv.org/abs/2304.15010v1
|
https://arxiv.org/pdf/2304.15010v1.pdf
|
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
|
How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize well to open-ended visual instructions and lags behind GPT-4. In this paper, we present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters (e.g., norm, bias and scale), which distribute the instruction-following ability across the entire LLaMA model besides adapters. Secondly, we propose an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced by optimizing disjoint groups of learnable parameters. This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset. During inference, we incorporate additional expert models (e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image understanding capability without incurring training costs. Compared to the original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal instructions by merely introducing 14M parameters over LLaMA. The newly designed framework also exhibits stronger language-only instruction-following capabilities and even excels in chat interactions. Our code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapter.
|
['Yu Qiao', 'Hongsheng Li', 'Xiangyu Yue', 'Conghui He', 'Pan Lu', 'Wei zhang', 'Aojun Zhou', 'Shijie Geng', 'Ziyi Lin', 'Renrui Zhang', 'Jiaming Han', 'Peng Gao']
|
2023-04-28
| null | null | null | null |
['optical-character-recognition', 'instruction-following']
|
['computer-vision', 'natural-language-processing']
|
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|
[10.910120964050293, 1.5943434238433838]
|
3130e2f1-6b3e-41ea-825d-ed022f642abf
|
trocr-transformer-based-optical-character
|
2109.10282
| null |
https://arxiv.org/abs/2109.10282v5
|
https://arxiv.org/pdf/2109.10282v5.pdf
|
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
|
Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at \url{https://aka.ms/trocr}.
|
['Yijuan Lu', 'Lei Cui', 'Jingye Chen', 'Furu Wei', 'Zhoujun Li', 'Cha Zhang', 'Dinei Florencio', 'Tengchao Lv', 'Minghao Li']
|
2021-09-21
| null | null | null | null |
['scene-text-recognition']
|
['computer-vision']
|
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|
[11.822325706481934, 2.154616594314575]
|
a676e746-eb0a-48e9-8287-55891c926698
|
which-and-where-to-focus-a-simple-yet
|
2109.03451
| null |
https://arxiv.org/abs/2109.03451v1
|
https://arxiv.org/pdf/2109.03451v1.pdf
|
Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images
|
Scene text detection has drawn the close attention of researchers. Though many methods have been proposed for horizontal and oriented texts, previous methods may not perform well when dealing with arbitrary-shaped texts such as curved texts. In particular, confusion problem arises in the case of nearby text instances. In this paper, we propose a simple yet effective method for accurate arbitrary-shaped nearby scene text detection. Firstly, a One-to-Many Training Scheme (OMTS) is designed to eliminate confusion and enable the proposals to learn more appropriate groundtruths in the case of nearby text instances. Secondly, we propose a Proposal Feature Attention Module (PFAM) to exploit more effective features for each proposal, which can better adapt to arbitrary-shaped text instances. Finally, we propose a baseline that is based on Faster R-CNN and outputs the curve representation directly. Equipped with PFAM and OMTS, the detector can achieve state-of-the-art or competitive performance on several challenging benchmarks.
|
['Weiping Wang', 'Xugong Qin', 'Yu Zhou', 'Youhui Guo']
|
2021-09-08
| null | null | null | null |
['scene-text-detection']
|
['computer-vision']
|
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|
[12.043935775756836, 2.216667652130127]
|
b3799523-3932-485f-ac15-f77b7e2f5dac
|
exploring-the-efficacy-of-pre-trained
|
2211.11216
| null |
https://arxiv.org/abs/2211.11216v2
|
https://arxiv.org/pdf/2211.11216v2.pdf
|
Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task
|
Benefiting from large-scale datasets and pre-trained models, the field of generative models has recently gained significant momentum. However, most datasets for symbolic music are very small, which potentially limits the performance of data-driven multimodal models. An intuitive solution to this problem is to leverage pre-trained models from other modalities (e.g., natural language) to improve the performance of symbolic music-related multimodal tasks. In this paper, we carry out the first study of generating complete and semantically consistent symbolic music scores from text descriptions, and explore the efficacy of using publicly available checkpoints (i.e., BERT, GPT-2, and BART) for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity. We analyse the capabilities and limitations of our model to better understand the potential of language-music models.
|
['Maosong Sun', 'Shangda Wu']
|
2022-11-21
| null | null | null | null |
['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation']
|
['audio', 'audio', 'music', 'music']
|
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|
[15.724515914916992, 5.255472183227539]
|
7d1ef13b-ee3d-4615-9580-64ea8eae11f4
|
lidar-based-localization-using-universal
| null | null |
https://www.sciencedirect.com/science/article/pii/S0031320322001662
|
https://www.sciencedirect.com/science/article/pii/S0031320322001662
|
LiDAR-based localization using universal encoding and memory-aware regression
|
Visual localization is critical to many robotics and computer vision applications. Absolute pose regression performs localization by encoding scene features followed by pose regression, which has achieved impressive results in localization. It recovers 6-DoF poses from captured scene data alone. However, current methods suffer from being retrained with specific source data whenever the scene changes, resulting in expensive computational costs, data privacy disclosure, and unreliable localization caused by the inability to memorize all data. In this paper, we propose a novel LiDAR-based absolute pose regression network with universal encoding to avoid redundant retraining and the loss of data privacy. Specifically, we propose using universal feature encoding for different scenes. Only the regressor needs to be retrained to achieve higher efficiency, and the training is performed using the encoded features without source data, which preserves data privacy. Then, we propose a memory regressor for memory-aware regression, where the hidden unit numbers in the regressor determine the memorization capacity. It can be used to derive and improve the upper bound of the capacity to enable more reliable localization. Then, it is possible to modify the regressor structure to adapt different memorization capacity requirements for different scene sizes. Extensive experiments on outdoor and indoor datasets validated the above analyses and demonstrated the effectiveness of the proposed method
|
['Shangshu Yu']
|
2022-08-01
| null | null | null |
pattern-recognition-2022-8
|
['visual-localization', 'memorization']
|
['computer-vision', 'natural-language-processing']
|
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|
[7.654376029968262, -2.17293643951416]
|
8c8a8e61-5d2c-40c3-b3b4-c1c6b8ebe96a
|
improving-knowledge-graph-representation
|
2112.04087
| null |
https://arxiv.org/abs/2112.04087v1
|
https://arxiv.org/pdf/2112.04087v1.pdf
|
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
|
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning framework for knowledge graph representation learning, in which a KG model is firstly pre-trained with triple classification task, followed by discriminative fine-tuning on specific downstream tasks such as entity type prediction and entity alignment. Drawing on the general ideas of learning deep contextualized word representations in typical pre-trained language models, we propose SCoP to learn pre-trained KG representations with structural and contextual triples of the target triple encoded. Experimental results demonstrate that fine-tuning SCoP not only outperforms results of baselines on a portfolio of downstream tasks but also avoids tedious task-specific model design and parameter training.
|
['Huajun Chen', 'Chen Hui', 'Chi Man Wong', 'Zhen Bi', 'Wen Zhang', 'Ganqiang Ye']
|
2021-12-08
| null | null | null | null |
['type-prediction', 'triple-classification']
|
['computer-code', 'graphs']
|
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|
[8.914484024047852, 7.939960479736328]
|
5fac4e0e-0b88-48fd-8a28-49e0d67af4eb
|
using-u-net-network-for-efficient-brain-tumor
|
2211.01885
| null |
https://arxiv.org/abs/2211.01885v1
|
https://arxiv.org/pdf/2211.01885v1.pdf
|
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
|
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.
|
['Soumyabrata Dev', 'Mayank Jain', 'Alice Othmani', 'Jason Walsh']
|
2022-11-03
| null | null | null | null |
['tumor-segmentation', 'brain-tumor-segmentation']
|
['computer-vision', 'medical']
|
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|
[14.452470779418945, -2.452648639678955]
|
c8f5ffff-c934-4f1c-befb-410ea5f566cf
|
deep-level-sets-implicit-surface
|
1901.06802
| null |
http://arxiv.org/abs/1901.06802v1
|
http://arxiv.org/pdf/1901.06802v1.pdf
|
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
|
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
|
['Jhony K. Pontes', 'Mateusz Michalkiewicz', 'Anders Eriksson', 'Mahsa Baktashmotlagh', 'Dominic Jack']
|
2019-01-21
| null | null | null | null |
['3d-shape-representation']
|
['computer-vision']
|
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|
[8.329948425292969, -3.58337664604187]
|
356c649a-6ea2-4daf-92f2-083de25daab9
|
bayesian-optimization-based-beam-alignment
|
2207.14174
| null |
https://arxiv.org/abs/2207.14174v1
|
https://arxiv.org/pdf/2207.14174v1.pdf
|
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems
|
Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair. During the BA procedure, this strategy exploits information from the measured beam pairs to predict the best beam pair. In addition, we suggest a novel BO algorithm based on the gradient boosting regression tree model. The simulation results demonstrate the spectral efficiency performance of our proposed schemes for BA using three different surrogate models. They also demonstrate that the proposed schemes can achieve spectral efficiency with a small overhead when compared to the orthogonal match pursuit (OMP) algorithm and the Thompson sampling-based multi-armed bandit (TS-MAB) method.
|
['Zhongpei Zhang', 'Zhiqin Hong', 'Baojuan Liu', 'Songjie Yang']
|
2022-07-28
| null | null | null | null |
['thompson-sampling']
|
['methodology']
|
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|
[6.402356147766113, 1.3409779071807861]
|
256b5933-a182-4a50-95e0-69c4acf1db40
|
fine-tuning-bert-with-character-level-noise
|
2303.17683
| null |
https://arxiv.org/abs/2303.17683v1
|
https://arxiv.org/pdf/2303.17683v1.pdf
|
Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages
|
In this work, we induce character-level noise in various forms when fine-tuning BERT to enable zero-shot cross-lingual transfer to unseen dialects and languages. We fine-tune BERT on three sentence-level classification tasks and evaluate our approach on an assortment of unseen dialects and languages. We find that character-level noise can be an extremely effective agent of cross-lingual transfer under certain conditions, while it is not as helpful in others. Specifically, we explore these differences in terms of the nature of the task and the relationships between source and target languages, finding that introduction of character-level noise during fine-tuning is particularly helpful when a task draws on surface level cues and the source-target cross-lingual pair has a relatively high lexical overlap with shorter (i.e., less meaningful) unseen tokens on average.
|
['David Chiang', 'Aarohi Srivastava']
|
2023-03-30
| null | null | null | null |
['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer']
|
['natural-language-processing', 'natural-language-processing']
|
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|
[10.864577293395996, 9.998495101928711]
|
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