pwc-archive/papers-with-abstracts huggingface dataset:
ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths
Authors: Yanhao Zhu, Zhineng Chen, Shuai Zhao, Hongtao Xie, Wenming Guo, Yongdong Zhang
Date: 2019-08-23 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e.g., symmetric contracting and expansive paths as well as lateral skip-connections. It remains a research direction to devise novel architectures to further benefit the segmentation. In this paper, we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths. In particular, we augment the paths by the recently proposed advanced techniques including ASPP, dense connection and deep supervision mechanisms, and novel connections such as directly connecting the raw image to the expansive side. With these augmentations, ACE-net can utilize features from multiple sources, scales and reception fields to segment while still maintains a relative simple architecture. Experiments on two typical biomedical segmentation tasks validate its effectiveness, where highly competitive results are obtained in both tasks while ACE-net still runs fast at inference.
Entity Suggestion by Example using a Conceptual Taxonomy
Authors: Yi Zhang, Yanghua Xiao, Seung-won Hwang, Haixun Wang, X. Sean Wang, Wei Wang
Date: 2015-11-29 00:00:00

Entity suggestion by example (ESbE) refers to a type of entity acquisition query in which a user provides a set of example entities as the query and obtains in return some entities that best complete the concept underlying the given query. Such entity acquisition queries can be useful in many applications such as related-entity recommendation and query expansion. A number of ESbE query processing solutions exist in the literature. However, they mostly build only on the idea of entity co-occurrences either in text or web lists, without taking advantage of the existence of many web-scale conceptual taxonomies that consist of hierarchical isA relationships between entity-concept pairs. This paper provides a query processing method based on the relevance models between entity sets and concepts. These relevance models can be used to obtain the fine-grained concepts implied by the query entity set, and the entities that belong to a given concept, thereby providing the entity suggestions. Extensive evaluations with real data sets show that the accuracy of the queries processed with this new method is significantly higher than that of existing solutions.
Self-reinforcing Unsupervised Matching
Authors: Jiang Lu, Lei LI, Chang-Shui Zhang
Date: 2019-08-23 00:00:00
Tasks: Continual Learning, Diversity, speech-recognition, Speech Recognition

Remarkable gains in deep learning usually rely on tremendous supervised data. Ensuring the modality diversity for one object in training set is critical for the generalization of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. This approach requires no any supervision in emerging modality and only one template in seen modality, providing a possible route towards continual learning.
A Three-Feature Model to Predict Colour Change Blindness
Authors: Steven Le Moan, Marius Pedersen
Date: 2019-08-25 00:00:00
Tasks: regression

Change blindness is a striking shortcoming of our visual system which is exploited in the popular "Spot the difference" game. It makes us unable to notice large visual changes happening right before our eyes and illustrates the fact that we see much less than we think we do. We introduce a fully automated model to predict colour change blindness in cartoon images based on two low-level image features and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty.
Machine and Deep Learning for Crowd Analytics
Authors: Muhammad Siraj
Date: 2019-08-25 00:00:00
Tasks: Deep Learning, Density Estimation

In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.
Learning Pose Grammar for Monocular 3D Pose Estimation
Authors: Yuanlu Xu, Wenguan Wang, Xiaobai Liu, Jianwen Xie, Song-Chun Zhu
Date: 2019-06-01 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2019 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Data Augmentation, Pose Estimation

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry,
motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.
Twin gaussian processes for structured prediction
Authors: Liefeng Bo, Cristian Sminchisescu
Date: 2010-03-01 00:00:00
Proceeding: International Journal of Computer Vision 2010 3
Tasks: 3D Human Pose Estimation, Camera Calibration, Gaussian Processes, Prediction, Structured Prediction

We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this should hold, on average, between their marginal distributions. TGP captures not only the interdependencies between covariates, as in a typical GP, but also those between responses, so correlations among both inputs and outputs are accounted for. TGP is exemplified, with promising results, for the reconstruction of 3d human poses from monocular and multicamera video sequences in the recently introduced HumanEva benchmark, where we achieve 5 cm error on average per 3d marker for models trained jointly, using data from multiple people and multiple activities. The method is fast and automatic: it requires no hand-crafting of the initial pose, camera calibration parameters, or the availability of a 3d body model associated with human subjects used for training or testing.
Depth sweep regression forests for estimating 3d human pose from images
Authors: Ilya Kostrikov, Juergen Gall
Date: 2014-09-01 00:00:00
Proceeding: In Proceedings British Machine Vision Conference 2014 (BMVC 2014) 2014 9
Tasks: 3D Human Pose Estimation, Position, regression

We address the problem of estimating the 3d pose from monocular images. However, instead of learning a regression from image features to the full pose, we regress the positions of the joints in 3d space and infer the pose using a 3d pictorial structure framework. For regression, we rely on regression forests that have been shown to efficiently predict 2d pose from images or 3d pose from depth data. These approaches, however, cannot be directly applied since each local image or depth feature estimates the relative positions of the joints from the feature location. While the relative position is well defined if feature and joint locations are given either in 2d or in 3d, it is, however, not defined if the features are sampled from 2d images without depth information and the joint locations need to be predicted in a 3d world coordinate system.
Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments
Authors: Catalin Ionescu, Dragos Papava, Vlad Olaru, Cristian Sminchisescu
Date: 2013-12-12 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 36 , Issue: 7 , July 2014 ) 2013 12
Tasks: 3D geometry, 3D Human Pose Estimation, Mixed Reality, Pose Estimation

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.
Predicting people’s 3D poses from short sequences
Authors: Bugra Tekin, Xiaolu Sun, Xinchao Wang, Vincent Lepetit, Pascal Fua
Date: 2015-04-30 00:00:00
Proceeding: arXiv:1504.08200 Search... Help | Advanced Search 2015 4
Tasks: 3D Human Pose Estimation

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Instead of computing candidate poses in individual frames and then linking them, as is often done, we regress directly from a spatio-temporal block of frames to a 3D pose in the central one. We will demonstrate that this approach allows us to effectively overcome ambiguities and to improve upon the state-of-the-art on challenging sequences.
Deep learning for decentralized parking lot occupancy detection
Authors: G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, C. Vairo
Date: 2017-04-15 00:00:00
Proceeding: Expert systems with applications 72 (2017) 2017 4
Tasks: Deep Learning

A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.
Local block-wise self attention for normal organ segmentation
Authors: Jue Jiang, Elguindi Sharif, Hyemin Um, Sean Berry, Harini Veeraraghavan
Date: 2019-09-11 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Organ Segmentation, Segmentation

We developed a new and computationally simple local block-wise self attention based normal structures segmentation approach applied to head and neck computed tomography (CT) images. Our method uses the insight that normal organs exhibit regularity in their spatial location and inter-relation within images, which can be leveraged to simplify the computations required to aggregate feature information. We accomplish this by using local self attention blocks that pass information between each other to derive the attention map. We show that adding additional attention layers increases the contextual field and captures focused attention from relevant structures. We developed our approach using U-net and compared it against multiple state-of-the-art self attention methods. All models were trained on 48 internal headneck CT scans and tested on 48 CT scans from the external public domain database of computational anatomy dataset. Our method achieved the highest Dice similarity coefficient segmentation accuracy of 0.85$\pm$0.04, 0.86$\pm$0.04 for left and right parotid glands, 0.79$\pm$0.07 and 0.77$\pm$0.05 for left and right submandibular glands, 0.93$\pm$0.01 for mandible and 0.88$\pm$0.02 for the brain stem with the lowest increase of 66.7\% computing time per image and 0.15\% increase in model parameters compared with standard U-net. The best state-of-the-art method called point-wise spatial attention, achieved \textcolor{black}{comparable accuracy but with 516.7\% increase in computing time and 8.14\% increase in parameters compared with standard U-net.} Finally, we performed ablation tests and studied the impact of attention block size, overlap of the attention blocks, additional attention layers, and attention block placement on segmentation performance.
Hybrid Cascaded Neural Network for Liver Lesion Segmentation
Authors: Raunak Dey, Yi Hong
Date: 2019-09-11 00:00:00
Tasks: Lesion Segmentation, Segmentation

Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper we propose a cascaded system that combines both 2D and 3D convolutional neural networks to effectively segment hepatic lesions. Our 2D network operates on a slice by slice basis to segment the liver and larger tumors, while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per case of 68.1%, which performs the best among all non pre-trained models and the second best among published methods. We also perform two-fold cross-validation to reveal the over- and under-segmentation issues in the LiTS annotations.
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Authors: Dennis Bontempi, Sergio Benini, Alberto Signoroni, Michele Svanera, Lars Muckli
Date: 2019-09-11 00:00:00
Tasks: Decoder, Segmentation, Weakly-supervised Learning, Weakly supervised segmentation

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This entails a loss of global contextual information thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed by 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements clearly prefer our solution with respect to the widely adopted atlas-based FreeSurfer software.
WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection
Authors: Zhaoyang Zeng, Bei Liu, Jianlong Fu, Hongyang Chao, Lei Zhang
Date: 2019-09-11 00:00:00
Proceeding: ICCV 2019 9
Tasks: Object, object-detection, Object Detection, Region Proposal, Weakly Supervised Object Detection

We study on weakly-supervised object detection (WSOD)which plays a vital role in relieving human involvement fromobject-level annotations.  Predominant works integrate re-gion proposal mechanisms with convolutional neural net-works (CNN). Although CNN is proficient in extracting dis-criminative  local  features,  grand  challenges  still  exist  tomeasure the likelihood of a bounding box containing a com-plete  object  (i.e.,  “objectness”).    In  this  paper,  we  pro-pose a novelWSODframework withObjectnessDistillation(i.e.,WSOD2) by designing a tailored training mechanismfor weakly-supervised object detection. Multiple regressiontargets  are  specifically  determined  by  jointly  consideringbottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptivelinear  combination.   As  bounding  box  regression  can  fa-cilitate a region proposal learning to approach its regres-sion target with high objectness during training, deep ob-jectness  representation  learned  from  bottom-up  evidencescan be gradually distilled into CNN by optimization.   Weexplore  different  adaptive  training  curves  for  BU/TD  ob-jectness, and show that the proposed WSOD2can achievestate-of-the-art results.
Safe Policy Improvement with an Estimated Baseline Policy
Authors: Thiago D. Simão, Romain Laroche, Rémi Tachet des Combes
Date: 2019-09-11 00:00:00
Tasks: Management, Reinforcement Learning

Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on the trained policy performance. However, in many real-world applications such as dialogue systems, pharmaceutical tests or crop management, data is collected under human supervision and the baseline remains unknown. In this paper, we apply SPIBB algorithms with a baseline estimate built from the data. We formally show safe policy improvement guarantees over the true baseline even without direct access to it. Our empirical experiments on finite and continuous states tasks support the theoretical findings. It shows little loss of performance in comparison with SPIBB when the baseline policy is given, and more importantly, drastically and significantly outperforms competing algorithms both in safe policy improvement, and in average performance.
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Authors: Qi Qian, Lei Shang, Baigui Sun, Juhua Hu, Hao Li, Rong Jin
Date: 2019-09-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Retrieval, Metric Learning, Triplet

Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization problem with triplet constraints. Due to the vast number of triplet constraints, a sampling strategy is essential for DML. With the tremendous success of deep learning in classifications, it has been applied for DML. When learning embeddings with deep neural networks (DNNs), only a mini-batch of data is available at each iteration. The set of triplet constraints has to be sampled within the mini-batch. Since a mini-batch cannot capture the neighbors in the original set well, it makes the learned embeddings sub-optimal. On the contrary, optimizing SoftMax loss, which is a classification loss, with DNN shows a superior performance in certain DML tasks. It inspires us to investigate the formulation of SoftMax. Our analysis shows that SoftMax loss is equivalent to a smoothed triplet loss where each class has a single center. In real-world data, one class can contain several local clusters rather than a single one, e.g., birds of different poses. Therefore, we propose the SoftTriple loss to extend the SoftMax loss with multiple centers for each class. Compared with conventional deep metric learning algorithms, optimizing SoftTriple loss can learn the embeddings without the sampling phase by mildly increasing the size of the last fully connected layer. Experiments on the benchmark fine-grained data sets demonstrate the effectiveness of the proposed loss function. Code is available at https://github.com/idstcv/SoftTriple