pwc-archive/papers-with-abstracts huggingface dataset:
Lifting 2d Human Pose to 3d : A WeaklySupervised Approach
Authors: Sandika Biswas, Sanjana Sinha, Kavya Gupta and Brojeshwar Bhowmick
Date: 2019-05-03 00:00:00
Proceeding: arXiv 2019 5
Tasks: 3D Pose Estimation, Pose Estimation

Lifting 2d Human Pose to 3d : A WeaklySupervised ApproachSandika Biswas, Sanjana Sinha, Kavya Gupta and Brojeshwar BhowmickEmbedded Systems and Robotics,TCS Research and InnovationEmail:{biswas.sandika, sanjana.sinha, gupta.kavya, b.bhowmick}@tcs.com,Abstract—Estimating 3d human pose from monocular imagesis  a  challenging  problem  due  to  the  variety  and  complexity  ofhuman  poses  and  the  inherent  ambiguity  in  recovering  depthfrom   the   single   view.   Recent   deep   learning   based   methodsshow   promising   results   by   using   supervised   learning   on   3dpose  annotated  datasets.  However,  the  lack  of  large-scale  3dannotated   training   data   captured   under   in-the-wild   settingsmakes the 3d pose estimation difficult for in-the-wild poses. Fewapproaches  have  utilized  training  images  from  both  3d  and  2dpose  datasets  in  a  weakly-supervised  manner  for  learning  3dposes  in  unconstrained  settings.  In  this  paper,  we  propose  amethod  which  can  effectively  predict  3d  human  pose  from  2dpose using a deep neural network trained in a weakly-supervisedmanner on a combination of ground-truth 3d pose and ground-truth 2d pose. Our method uses re-projection error minimizationas  a  constraint  to  predict  the  3d  locations  of  body  joints,  andthis is crucial for training on data where the 3d ground-truth isnot present. Since minimizing re-projection error alone may notguarantee an accurate 3d pose, we also use additional geometricconstraints  on  skeleton  pose  to  regularize  the  pose  in  3d.  Wedemonstrate the superior generalization ability of our method bycross-dataset  validation  on  a  challenging  3d  benchmark  datasetMPI-INF-3DHP  containing  in  the  wild  3d  pose
Medical Image Synthesis with Deep Convolutional Adversarial Networks
Authors: Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, and Dinggang Shen, Fellow, IEEE
Date: 2018-03-09 00:00:00
Proceeding: IEEE Transactions on Biomedical Engineering 2018 3
Tasks: Image Generation

Medical imaging plays a critical role in various
clinical applications. However, due to multiple considera- tions such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical im- age synthesis can be of great benefit by estimating a de- sired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss func- tion to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the net- work. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Ex- perimental results show that our method is accurate and robust for synthesizing target images from the correspond- ing source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.
Index Terms—Adversarial learning, auto-context model, deep learning, image synthesis, residual learning. I.
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices
Authors: Cong Wang, Yanru Xiao, Xing Gao, Li Li, Jun Wang
Date: 2020-05-26 00:00:00
Tasks: Metric Learning, Multi-class Classification

Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual's unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95% on three public datasets, a sheer 15% gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99% and 90% success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Authors: Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen
Date: 2020-05-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: model, Model-based Reinforcement Learning, Reinforcement Learning (RL)

This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider $\gamma$-discounted infinite-horizon Markov decision processes (MDPs) with state space $\mathcal{S}$ and action space $\mathcal{A}$. Despite a number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy is yet to be determined. In particular, all prior results suffer from a severe sample size barrier, in the sense that their claimed statistical guarantees hold only when the sample size exceeds at least $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^2}$. The current paper overcomes this barrier by certifying the minimax optimality of two algorithms -- a perturbed model-based algorithm and a conservative model-based algorithm -- as soon as the sample size exceeds the order of $\frac{|\mathcal{S}||\mathcal{A}|}{1-\gamma}$ (modulo some log factor). Moving beyond infinite-horizon MDPs, we further study time-inhomogeneous finite-horizon MDPs, and prove that a plain model-based planning algorithm suffices to achieve minimax-optimal sample complexity given any target accuracy level. To the best of our knowledge, this work delivers the first minimax-optimal guarantees that accommodate the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically infeasible).
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
Authors: Tae Hwan Oh, Ji Yoon Han, Hyonsu Choe, Seokwon Park, Han He, Jinho D. Choi, Na-Rae Han, Jena D. Hwang, Hansaem Kim
Date: 2020-05-26 00:00:00
Proceeding: WS 2020 7

In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word-order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.
Minimizing Supervision in Multi-label Categorization
Authors: Rajat, Munender Varshney, Pravendra Singh, Vinay P. Namboodi
Date: 2020-05-26 00:00:00
Tasks: Active Learning, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.
A reinforcement learning approach to rare trajectory sampling
Authors: Dominic C. Rose, Jamie F. Mair, Juan P. Garrahan
Date: 2020-05-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to access in a statistically significant way. What are required are strategies to "make rare events typical" so that they can be generated on demand. Here we present such a general approach to adaptively construct a dynamics that efficiently samples atypical events. We do so by exploiting the methods of reinforcement learning (RL), which refers to the set of machine learning techniques aimed at finding the optimal behaviour to maximise a reward associated with the dynamics. We consider the general perspective of dynamical trajectory ensembles, whereby rare events are described in terms of ensemble reweighting. By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest. As simple illustrations we consider in detail the problem of excursions of a random walker, for the case of rare events with a finite time horizon; and the problem of a studying current statistics of a particle hopping in a ring geometry, for the case of an infinite time horizon. We discuss natural extensions of the ideas presented here, including to continuous-time Markov systems, first passage time problems and non-Markovian dynamics.
Refining Implicit Argument Annotation for UCCA
Authors: Ruixiang Cui, Daniel Hershcovich
Date: 2020-05-26 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Coreference Resolution, Natural Language Understanding, Question Answering, Sentence

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.
Learning Local Features with Context Aggregation for Visual Localization
Authors: Siyu Hong, Kunhong Li, Yongcong Zhang, Zhiheng Fu, Mengyi Liu, Yulan Guo
Date: 2020-05-26 00:00:00
Tasks: Keypoint Detection, Visual Localization

Keypoint detection and description is fundamental yet important in many vision applications. Most existing methods use detect-then-describe or detect-and-describe strategy to learn local features without considering their context information. Consequently, it is challenging for these methods to learn robust local features. In this paper, we focus on the fusion of low-level textual information and high-level semantic context information to improve the discrimitiveness of local features. Specifically, we first estimate a score map to represent the distribution of potential keypoints according to the quality of descriptors of all pixels. Then, we extract and aggregate multi-scale high-level semantic features based by the guidance of the score map. Finally, the low-level local features and high-level semantic features are fused and refined using a residual module. Experiments on the challenging local feature benchmark dataset demonstrate that our method achieves the state-of-the-art performance in the local feature challenge of the visual localization benchmark.
End-to-End Object Detection with Transformers
Authors: Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
Date: 2020-05-26 00:00:00
Proceeding: ECCV 2020 8
Tasks: 2D Object Detection, Decoder, Object, Object Detection, Panoptic Segmentation, Real-Time Object Detection

We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. Training code and pretrained models are available at https://github.com/facebookresearch/detr.
Time-Variant Variational Transfer for Value Functions
Authors: Giuseppe Canonaco, Andrea Soprani, Manuel Roveri, Marcello Restelli
Date: 2020-05-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary. Therefore, the target and source tasks are i.i.d. samples of the same distribution. In the context of this work, we consider the problem of transferring value functions through a variational method when the distribution that generates the tasks is time-variant, proposing a solution that leverages this temporal structure inherent in the task generating process. Furthermore, by means of a finite-sample analysis, the previously mentioned solution is theoretically compared to its time-invariant version. Finally, we will provide an experimental evaluation of the proposed technique with three distinct temporal dynamics in three different RL environments.
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
Authors: Alexander Wong, Zhong Qiu Lin, Linda Wang, Audrey G. Chung, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Timothy Q. Duong
Date: 2020-05-26 00:00:00

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0.664 $\pm$ 0.032 and 0.635 $\pm$ 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing networks achieved R$^2$ of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. Interpretation: The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.
Approximation Schemes for ReLU Regression
Authors: Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi
Date: 2020-05-26 00:00:00
Tasks: regression

We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distribution. We give the first efficient, constant-factor approximation algorithm for this problem assuming the underlying distribution satisfies some weak concentration and anti-concentration conditions (and includes, for example, all log-concave distributions). This solves the main open problem of Goel et al., who proved hardness results for any exact algorithm for ReLU regression (up to an additive $\epsilon$). Using more sophisticated techniques, we can improve our results and obtain a polynomial-time approximation scheme for any subgaussian distribution. Given the aforementioned hardness results, these guarantees can not be substantially improved. Our main insight is a new characterization of surrogate losses for nonconvex activations. While prior work had established the existence of convex surrogates for monotone activations, we show that properties of the underlying distribution actually induce strong convexity for the loss, allowing us to relate the global minimum to the activation's Chow parameters.
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
Authors: Bo Li, Marius de Groot, Rebecca M. E. Steketee, Rozanna Meijboom, Marion Smits, Meike W. Vernooij, M. Arfan Ikram, Jiren Liu, Wiro J. Niessen, Esther E. Bron
Date: 2020-05-26 00:00:00
Tasks: Diffusion  MRI, Segmentation

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.