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20192022
most citedEarly Action Prediction with Generative Adversarial Networks

34 citations · 72 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV20222 cited

D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction

Tingyu Fan, Linyao Gao, Yiling Xu +2

The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D…

cs.CV20203 cited

Temporal Relational Modeling with Self-Supervision for Action Segmentation

Dong Wang, Di Hu, Xingjian Li +1

Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) ha…

cs.CV202033 cited

Curriculum Audiovisual Learning

Di Hu, Zheng Wang, Haoyi Xiong +3

Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data. In this paper, we present a flexible…

cs.CV2019

signADAM: Learning Confidences for Deep Neural Networks

Dong Wang, Yicheng Liu, Wenwo Tang +4

In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks. We first introduce the sign operation of stochastic gradients (as in sign-based…

cs.CV2019

A One-step Pruning-recovery Framework for Acceleration of Convolutional Neural Networks

Dong Wang, Lei Zhou, Xiao Bai +1

Acceleration of convolutional neural network has received increasing attention during the past several years. Among various acceleration techniques, filter pruning has its inherent…

cs.CV201934 cited

Early Action Prediction with Generative Adversarial Networks

Dong Wang, Yuan Yuan, Qi Wang

Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic acciden…