pwc-archive/papers-with-abstracts huggingface dataset:
Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models
Authors: Jasmin Hundall, Benson A. Babu
Date: 2020-03-22 00:00:00
Tasks: BIG-bench Machine Learning

Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait assessment. Computer vision is beneficial in gait analysis, it helps capture the patient posture. Several literature suggests the use of different machine learning algorithms such as SVM, ANN, K-Star, Random Forest, KNN, among others to perform the classification on the features extracted to study patient gait abnormalities.
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques
Authors: Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch
Date: 2020-03-20 00:00:00
Tasks: Deep Learning, Denoising

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transmitting fewer image data at the sender's end. At the receiver's end, we propose a DC recovery algorithm together with the deep residual learning framework to recover images with high quality. The second work is about adversarial examples defenses based on signal processing. We propose the wavelet extension method to extend image data features, which makes it more difficult to generate adversarial examples. We further adopt wavelet denoising to reduce the influence of the adversarial perturbations. With intensive experiments, we demonstrate that both works are effective in their application scenarios.
The current state of automated negotiation theory: a literature review
Authors: Sam Vente, Angelika Kimmig, Alun Preece, Federico Cerutti
Date: 2020-03-30 00:00:00
Tasks: Survey

Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting. Automated negotiation has already seen increased usage in fields such as e-commerce and power distribution in smart girds, and recent advancements in opponent modelling have proven to deliver better outcomes. However, significant barriers to more widespread adoption remain, such as lack of predictable outcome over time and user trust. Additionally, there have been many recent advancements in the field of reasoning about uncertainty, which could help alleviate both those problems. As there is no recent survey on these two fields, and specifically not on their possible intersection we aim to provide such a survey here.
Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams
Authors: Sibo Zhang, Yuan Cheng, Deyuan Ke
Date: 2017-08-19 00:00:00
Tasks: Event Detection

The local event detection is to use posting messages with geotags on social networks to reveal the related ongoing events and their locations. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from large geo-tagged tweet streams in real time remains challenging. A robust and efficient cloud-based real-time local event detection software system would benefit various aspects in the real-life society, from shopping recommendation for customer service providers to disaster alarming for emergency departments. We use the preliminary research GeoBurst as a starting point, which proposed a novel method to detect local events. GeoBurst+ leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract related tweets to form candidate events. It further summarises the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. We mainly implement a website demonstration system Event-Radar with an improved algorithm to show the real-time local events online for public interests. Better still, as the query window shifts, our method can update the event list with little time cost, thus achieving continuous monitoring of the stream.
Inferring COVID-19 spreading rates and potential change points for case number forecasts
Authors: Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Michael Wibral, Joao Pinheiro Neto, Michael Wilczek, Viola Priesemann
Date: 2020-04-02 00:00:00
Tasks: Bayesian Inference

As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A main challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change as first governmental intervention measures are showing an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. For the case of COVID-19 spreading in Germany, we detect change points in the effective growth rate which correlate well with the times of publicly announced interventions. Thereby, we can (a) quantify the effects of recent governmental measures to mitigating the disease spread, and (b) incorporate analogue change points to forecast future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.
Feature Pyramid Grids
Authors: Kai Chen, Yuhang Cao, Chen Change Loy, Dahua Lin, Christoph Feichtenhofer
Date: 2020-04-07 00:00:00
Tasks: Neural Architecture Search, object-detection, Object Detection, Object Recognition

Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition.
Event Based, Near Eye Gaze Tracking Beyond 10,000Hz
Authors: Anastasios N. Angelopoulos, Julien N. P. Martel, Amit P. S. Kohli, Jorg Conradt, Gordon Wetzstein
Date: 2020-04-07 00:00:00

The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our system builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We develop an online 2D pupil fitting method that updates a parametric model every one or few events. Moreover, we propose a polynomial regressor for estimating the point of gaze from the parametric pupil model in real time. Using the first event-based gaze dataset, available at https://github.com/aangelopoulos/event_based_gaze_tracking , we demonstrate that our system achieves accuracies of 0.45 degrees--1.75 degrees for fields of view from 45 degrees to 98 degrees. With this technology, we hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality.
Neural Analogical Matching
Authors: Maxwell Crouse, Constantine Nakos, Ibrahim Abdelaziz, Kenneth Forbus
Date: 2020-04-07 00:00:00

Analogy is core to human cognition. It allows us to solve problems based on prior experience, it governs the way we conceptualize new information, and it even influences our visual perception. The importance of analogy to humans has made it an active area of research in the broader field of artificial intelligence, resulting in data-efficient models that learn and reason in human-like ways. While cognitive perspectives of analogy and deep learning have generally been studied independently of one another, the integration of the two lines of research is a promising step towards more robust and efficient learning techniques. As part of a growing body of research on such an integration, we introduce the Analogical Matching Network: a neural architecture that learns to produce analogies between structured, symbolic representations that are largely consistent with the principles of Structure-Mapping Theory.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
Authors: Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao
Date: 2020-04-07 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, 3D Object Detection From Stereo Images, Disparity Estimation, object-detection, Object Detection, Vehicle Pose Estimation

In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.
Latent Network Structure Learning from High Dimensional Multivariate Point Processes
Authors: Biao Cai, Jingfei Zhang, Yongtao Guan
Date: 2020-04-07 00:00:00
Tasks: Functional Connectivity, Point Processes, Vocal Bursts Intensity Prediction

Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, we might wish to estimate the neuronal functional connectivity network based on spiking times recorded from a collection of neurons. To characterize the complex processes underlying the observed data, we propose a new and flexible class of nonstationary Hawkes processes that allow both excitatory and inhibitory effects. We estimate the latent network structure using an efficient sparse least squares estimation approach. Using a thinning representation, we establish concentration inequalities for the first and second order statistics of the proposed Hawkes process. Such theoretical results enable us to establish the non-asymptotic error bound and the selection consistency of the estimated parameters. Furthermore, we describe a least squares loss based statistic for testing if the background intensity is constant in time. We demonstrate the efficacy of our proposed method through simulation studies and an application to a neuron spike train data set.
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
Authors: Changmao Li, Jinho D. Choi
Date: 2020-04-07 00:00:00
Proceeding: ACL 2020 6
Tasks: Language Modeling, Language Modelling, Multi-Task Learning, Prediction, Question Answering

We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multi-task learning between the utterance prediction and the token span prediction is applied to fine-tune for span-based question answering (QA). Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.
Entity Linking via Dual and Cross-Attention Encoders
Authors: Oshin Agarwal, Daniel M. Bikel
Date: 2020-04-07 00:00:00
Tasks: Entity Linking, Entity Retrieval, Retrieval

Entity Linking has two main open areas of research: 1) generate candidate entities without using alias tables and 2) generate more contextual representations for both mentions and entities. Recently, a solution has been proposed for the former as a dual-encoder entity retrieval system (Gillick et al., 2019) that learns mention and entity representations in the same space, and performs linking by selecting the nearest entity to the mention in this space. In this work, we use this retrieval system solely for generating candidate entities. We then rerank the entities by using a cross-attention encoder over the target mention and each of the candidate entities. Whereas a dual encoder approach forces all information to be contained in the small, fixed set of vector dimensions used to represent mentions and entities, a crossattention model allows for the use of detailed information (read: features) from the entirety of each <mention, context, candidate entity> tuple. We experiment with features used in the reranker including different ways of incorporating document-level context. We achieve state-of-the-art results on TACKBP-2010 dataset, with 92.05% accuracy. Furthermore, we show how the rescoring model generalizes well when trained on the larger CoNLL-2003 dataset and evaluated on TACKBP-2010.
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations
Authors: Muhammad Asif Ali, Yifang Sun, Bing Li, Wei Wang
Date: 2020-04-07 00:00:00
Tasks: Entity Typing, Sentence

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FGNET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention sentence-specific context. This is inadequate for highly overlapping and noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively.
Capsule Networks -- A Probabilistic Perspective
Authors: Lewis Smith, Lisa Schut, Yarin Gal, Mark van der Wilk
Date: 2020-04-07 00:00:00
Tasks: Object

'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. This modelling assumption should lead to robustness to viewpoint changes since the sub-object/super-object relationships are invariant to the poses of the object. We describe a probabilistic generative model which encodes such capsule assumptions, clearly separating the generative parts of the model from the inference mechanisms. With a variational bound we explore the properties of the generative model independently of the approximate inference scheme, and gain insights into failures of the capsule assumptions and inference amortisation. We experimentally demonstrate the applicability of our unified objective, and demonstrate the use of test time optimisation to solve problems inherent to amortised inference in our model.
Temporal Pyramid Network for Action Recognition
Authors: Ceyuan Yang, Yinghao Xu, Jianping Shi, Bo Dai, Bolei Zhou
Date: 2020-04-07 00:00:00
Proceeding: CVPR 2020 6
Tasks: Action Recognition

Visual tempo characterizes the dynamics and the temporal scale of an action. Modeling such visual tempos of different actions facilitates their recognition. Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle. In this work we propose a generic Temporal Pyramid Network (TPN) at the feature-level, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner. Two essential components of TPN, the source of features and the fusion of features, form a feature hierarchy for the backbone so that it can capture action instances at various tempos. TPN also shows consistent improvements over other challenging baselines on several action recognition datasets. Specifically, when equipped with TPN, the 3D ResNet-50 with dense sampling obtains a 2% gain on the validation set of Kinetics-400. A further analysis also reveals that TPN gains most of its improvements on action classes that have large variances in their visual tempos, validating the effectiveness of TPN.
Unsupervised Person Re-identification via Softened Similarity Learning
Authors: Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, Qi Tian
Date: 2020-04-07 00:00:00
Proceeding: CVPR 2020 6
Tasks: Clustering, General Classification, Person Re-Identification, Quantization, Unsupervised Person Re-Identification

Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterative clustering and classification, so that unlabeled images are clustered into pseudo classes for a classifier to get trained, and the updated features are used for clustering and so on. This approach suffers two problems, namely, the difficulty of determining the number of clusters, and the hard quantization loss in clustering. In this paper, we follow the iterative training mechanism but discard clustering, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task. With these improvements, our approach becomes more elegant and is more robust to hyper-parameter changes. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance under the unsupervised re-ID setting.
Dense Regression Network for Video Grounding
Authors: Runhao Zeng, Haoming Xu, Wenbing Huang, Peihao Chen, Mingkui Tan, Chuang Gan
Date: 2020-04-07 00:00:00
Proceeding: CVPR 2020 6
Tasks: Natural Language Moment Retrieval, Natural Language Queries, regression, Video Grounding

We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS).
Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications
Authors: María J. Gómez-Silva
Date: 2020-04-07 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

The automatization of Multi-Object Tracking becomes a demanding task in real unconstrained scenarios, where the algorithms have to deal with crowds, crossing people, occlusions, disappearances and the presence of visually similar individuals. In those circumstances, the data association between the incoming detections and their corresponding identities could miss some tracks or produce identity switches. In order to reduce these tracking errors, and even their propagation in further frames, this article presents a Deep Multi-Shot neural model for measuring the Degree of Appearance Similarity (MS-DoAS) between person observations. This model provides temporal consistency to the individuals' appearance representation, and provides an affinity metric to perform frame-by-frame data association, allowing online tracking. The model has been deliberately trained to be able to manage the presence of previous identity switches and missed observations in the handled tracks. With that purpose, a novel data generation tool has been designed to create training tracklets that simulate such situations. The model has demonstrated a high capacity to discern when a new observation corresponds to a certain track, achieving a classification accuracy of 97\% in a hard test that simulates tracks with previous mistakes. Moreover, the tracking efficiency of the model in a Surveillance application has been demonstrated by integrating that into the frame-by-frame association of a Tracking-by-Detection algorithm.
Pooling in Graph Convolutional Neural Networks
Authors: Mark Cheung, John Shi, Lavender Yao Jiang, Oren Wright, José M. F. Moura
Date: 2020-04-07 00:00:00
Tasks: Classification, General Classification, Graph Classification

Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three different architectures: GCN, TAGCN, and GraphSAGE. We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy than GCN and GraphSAGE, particularly for datasets with larger and sparser graph structures.