pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
Authors: Xudong Wang, Ziwei Liu, Stella X. Yu
Date: 2020-08-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Contrastive Learning, Semi-Supervised Image Classification, Transfer Learning, Unsupervised Image Classification

Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be highly correlated and long-tail distributed. Natural between-instance similarity conflicts with the presumed instance distinction, causing unstable training and poor performance. Our idea is to discover and integrate between-instance similarity into contrastive learning, not directly by instance grouping, but by cross-level discrimination (CLD) between instances and local instance groups. While invariant mapping of each instance is imposed by attraction within its augmented views, between-instance similarity could emerge from common repulsion against instance groups. Our batch-wise and cross-view comparisons also greatly improve the positive/negative sample ratio of contrastive learning and achieve better invariant mapping. To effect both grouping and discrimination objectives, we impose them on features separately derived from a shared representation. In addition, we propose normalized projection heads and unsupervised hyper-parameter tuning for the first time. Our extensive experimentation demonstrates that CLD is a lean and powerful add-on to existing methods such as NPID, MoCo, InfoMin, and BYOL on highly correlated, long-tail, or balanced datasets. It not only achieves new state-of-the-art on self-supervision, semi-supervision, and transfer learning benchmarks, but also beats MoCo v2 and SimCLR on every reported performance attained with a much larger compute. CLD effectively brings unsupervised learning closer to natural data and real-world applications. Our code is publicly available at: https://github.com/frank-xwang/CLD-UnsupervisedLearning.
Diverse Group Formation Based on Multiple Demographic Features
Authors: Mohammed Alqahtani, Susan Gauch, Omar Salman, Mohammed Ibrahim, Reem Al-Saffar
Date: 2020-08-09 00:00:00
Tasks: Diversity

The goal of group formation is to build a team to accomplish a specific task. Algorithms are employed to improve the effectiveness of the team so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals expertise for expert recommendation and or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process. We propose a novel method to represent experts demographic profiles based on multidimensional demographic features. Moreover, we introduce two diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple multivalued demographic attributes simultaneously. We evaluate our proposed algorithms using a real dataset based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility.
Neural Light Transport for Relighting and View Synthesis
Authors: Xiuming Zhang, Sean Fanello, Yun-Ta Tsai, Tiancheng Sun, Tianfan Xue, Rohit Pandey, Sergio Orts-Escolano, Philip Davidson, Christoph Rhemann, Paul Debevec, Jonathan T. Barron, Ravi Ramamoorthi, William T. Freeman
Date: 2020-08-09 00:00:00

The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel views under arbitrary lighting. In this paper, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach to learn a neural representation of LT that is embedded in the space of a texture atlas of known geometric properties, and model all non-diffuse and global LT as residuals added to a physically-accurate diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination, while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse, previously seen observations. Qualitative and quantitative experiments demonstrate that our neural LT (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without separate treatment for both problems that prior work requires.
SpeedySpeech: Efficient Neural Speech Synthesis
Authors: Jan Vainer, Ondřej Dušek
Date: 2020-08-09 00:00:00
Tasks: Audio Synthesis, CPU, GPU, Speech Synthesis

While recent neural sequence-to-sequence models have greatly improved the quality of speech synthesis, there has not been a system capable of fast training, fast inference and high-quality audio synthesis at the same time. We propose a student-teacher network capable of high-quality faster-than-real-time spectrogram synthesis, with low requirements on computational resources and fast training time. We show that self-attention layers are not necessary for generation of high quality audio. We utilize simple convolutional blocks with residual connections in both student and teacher networks and use only a single attention layer in the teacher model. Coupled with a MelGAN vocoder, our model's voice quality was rated significantly higher than Tacotron 2. Our model can be efficiently trained on a single GPU and can run in real time even on a CPU. We provide both our source code and audio samples in our GitHub repository.
Spatiotemporal Contrastive Video Representation Learning
Authors: Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui
Date: 2020-08-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Recognition, Contrastive Learning, Data Augmentation, Representation Learning, Self-Supervised Action Recognition, Self-Supervised Action Recognition Linear, Self-Supervised Learning, Unsupervised Pre-training

We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial. We carefully design data augmentations involving spatial and temporal cues. Concretely, we propose a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. We also propose a sampling-based temporal augmentation method to avoid overly enforcing invariance on clips that are distant in time. On Kinetics-600, a linear classifier trained on the representations learned by CVRL achieves 70.4% top-1 accuracy with a 3D-ResNet-50 (R3D-50) backbone, outperforming ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8% using the same inflated R3D-50. The performance of CVRL can be further improved to 72.9% with a larger R3D-152 (2x filters) backbone, significantly closing the gap between unsupervised and supervised video representation learning. Our code and models will be available at https://github.com/tensorflow/models/tree/master/official/.
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
Authors: Manel Slokom, Martha Larson, Alan Hanjalic
Date: 2020-08-09 00:00:00
Tasks: Recommendation Systems, Synthetic Data Generation

This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation approach to hide specific information in the user-item matrix. We apply a transformation to the original data that changes some values, but leaves others the same. The result is a partially synthetic data set that can be used for recommendation but contains less specific information about individual user preferences. Synthetic data has the potential to be useful for companies, who are interested in releasing data to allow outside parties to develop new recommender algorithms, i.e., in the case of a recommender system challenge, and also reducing the risks associated with data misappropriation. Our experiments run a set of recommender system algorithms on our partially synthetic data sets as well as on the original data. The results show that the relative performance of the algorithms on the partially synthetic data reflects the relative performance on the original data. Further analysis demonstrates that properties of the original data are preserved under synthesis, but that for certain examples of attributes accessible in the original data are hidden in the synthesized data.
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
Authors: Yi-Fan Song, Zhang Zhang, Caifeng Shan, Liang Wang
Date: 2020-08-09 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional network (GCN) is proposed to explore sufficient discriminative features spreading over all skeleton joints, so that the distributed redundant representation reduces the sensitivity of the action models to non-standard skeletons. Concretely, the backbone GCN is extended by a series of ordered streams which is responsible for learning discriminative features from the joints less activated by preceding streams. Here, the activation degrees of skeleton joints of each GCN stream are measured by the class activation maps (CAM), and only the information from the unactivated joints will be passed to the next stream, by which rich features over all active joints are obtained. Thus, the proposed method is termed richly activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120 datasets. More crucially, on the synthetic occlusion and jittering datasets, the performance deterioration due to the occluded and disturbed joints can be significantly alleviated by utilizing the proposed RA-GCN.
Accurate Detection of Wake Word Start and End Using a CNN
Authors: Christin Jose, Yuriy Mishchenko, Thibaud Senechal, Anish Shah, Alex Escott, Shiv Vitaladevuni
Date: 2020-08-09 00:00:00

Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \textit{wake word} as it is used to wake up voice assistant enabled devices. Together with wake word detection, accurate estimation of wake word endpoints (start and end) is an important task of KWS. In this paper, we propose two new methods for detecting the endpoints of wake words in neural KWS that use single-stage word-level neural networks. Our results show that the new techniques give superior accuracy for detecting wake words' endpoints of up to 50 msec standard error versus human annotations, on par with the conventional Acoustic Model plus HMM forced alignment. To our knowledge, this is the first study of wake word endpoints detection methods for single-stage neural KWS.
3D Human Motion Estimation via Motion Compression and Refinement
Authors: Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani
Date: 2020-08-09 00:00:00
Tasks: 3D Human Pose Estimation

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motion estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.
A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification
Authors: Madhu Kiran, Amran Bhuiyan, Louis-Antoine Blais-Morin, Mehrsan Javan, Ismail Ben Ayed, Eric Granger
Date: 2020-08-09 00:00:00
Tasks: Optical Flow Estimation, Person Re-Identification, Video-Based Person Re-Identification

Person Re-Identification (ReID) is a challenging problem in many video analytics and surveillance applications, where a person's identity must be associated across a distributed non-overlapping network of cameras. Video-based person ReID has recently gained much interest because it allows capturing discriminant spatio-temporal information from video clips that is unavailable for image-based ReID. Despite recent advances, deep learning (DL) models for video ReID often fail to leverage this information to improve the robustness of feature representations. In this paper, the motion pattern of a person is explored as an additional cue for ReID. In particular, a flow-guided Mutual Attention network is proposed for fusion of image and optical flow sequences using any 2D-CNN backbone, allowing to encode temporal information along with spatial appearance information. Our Mutual Attention network relies on the joint spatial attention between image and optical flow features maps to activate a common set of salient features across them. In addition to flow-guided attention, we introduce a method to aggregate features from longer input streams for better video sequence-level representation. Our extensive experiments on three challenging video ReID datasets indicate that using the proposed Mutual Attention network allows to improve recognition accuracy considerably with respect to conventional gated-attention networks, and state-of-the-art methods for video-based person ReID.
Neural Manipulation Planning on Constraint Manifolds
Authors: Ahmed H. Qureshi, Jiangeng Dong, Austin Choe, Michael C. Yip
Date: 2020-08-09 00:00:00
Tasks: Motion Planning

The presence of task constraints imposes a significant challenge to motion planning. Despite all recent advancements, existing algorithms are still computationally expensive for most planning problems. In this paper, we present Constrained Motion Planning Networks (CoMPNet), the first neural planner for multimodal kinematic constraints. Our approach comprises the following components: i) constraint and environment perception encoders; ii) neural robot configuration generator that outputs configurations on/near the constraint manifold(s), and iii) a bidirectional planning algorithm that takes the generated configurations to create a feasible robot motion trajectory. We show that CoMPNet solves practical motion planning tasks involving both unconstrained and constrained problems. Furthermore, it generalizes to new unseen locations of the objects, i.e., not seen during training, in the given environments with high success rates. When compared to the state-of-the-art constrained motion planning algorithms, CoMPNet outperforms by order of magnitude improvement in computational speed with a significantly lower variance.
SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!
Authors: Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, Viswanath Pulabaigari, Bjorn Gamback
Date: 2020-08-09 00:00:00
Tasks: Emotion Recognition

Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, the computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twiter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes, with human-annotated labels namely sentiment (positive, negative, neutral), type of emotion (sarcastic, funny, offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humour, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
Authors: Zhi Huang, Paul Salama, Wei Shao, Jie Zhang, Kun Huang
Date: 2020-08-09 00:00:00
Tasks: regression

One of the central goals in precision health is the understanding and interpretation of high-dimensional biological data to identify genes and markers associated with disease initiation, development, and outcomes. Though significant effort has been committed to harness gene expression data for multiple analyses while accounting for time-to-event modeling by including survival times, many traditional analyses have focused separately on non-negative matrix factorization (NMF) of the gene expression data matrix and survival regression with Cox proportional hazards model. In this work, Cox proportional hazards regression is integrated with NMF by imposing survival constraints. This is accomplished by jointly optimizing the Frobenius norm and partial log likelihood for events such as death or relapse. Simulation results on synthetic data demonstrated the superiority of the proposed method, when compared to other algorithms, in finding survival associated gene clusters. In addition, using human cancer gene expression data, the proposed technique can unravel critical clusters of cancer genes. The discovered gene clusters reflect rich biological implications and can help identify survival-related biomarkers. Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression
Authors: Yu Guo, Yuxu Lu, Ryan Wen Liu, Meifang Yang, Kwok Tai Chui
Date: 2020-08-09 00:00:00
Tasks: Denoising, Image Enhancement, Management

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this paper, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.
A methodology for the measurement of track geometry based on computer vision and inertial sensors
Authors: José L. Escalona
Date: 2020-08-09 00:00:00

This document describes the theory used for the calculation of track geometric irregularities on a Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The main features of the proposed system are: 1. It is capable to measure track alignment, vertical profile, cross-level, gauge, twist and rail-head profile using non-contact technology. 2. It can be installed in line railway vehicles. It is compact and low cost. Provided that the equipment sees the rail heads when the vehicle is moving, it can be installed in any body of the vehicle: at the wheelsets level, above primary suspension (bogie frame) or above the secondary suspension (car body).