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20182021
most citedFew-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

15 citations · 24 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2021

Will You Ever Become Popular? Learning to Predict Virality of Dance Clips

Jiahao Wang, Yunhong Wang, Nina Weng +4

Dance challenges are going viral in video communities like TikTok nowadays. Once a challenge becomes popular, thousands of short-form videos will be uploaded in merely a couple of…

cs.CV2021

Video Person Re-identification using Attribute-enhanced Features

Tianrui Chai, Zhiyuan Chen, Annan Li +3

Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task. Pedestrian attribute, suc…

cs.CV202115 cited

Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

Jiahao Wang, Yunhong Wang, Sheng Liu +1

Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare…

cs.CV20211 cited

Silhouette based View embeddings for Gait Recognition under Multiple Views

Tianrui Chai, Xinyu Mei, Annan Li +1

Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information o…

cs.CV20208 cited

Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification

Zhiyuan Chen, Annan Li, Shilu Jiang +1

Video-based person re-identification (Re-ID) is an important computer vision task. The batch-hard triplet loss frequently used in video-based person Re-ID suffers from the Distance…

cs.CV2019

A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition

Zhiyuan Chen, Annan Li, Yunhong Wang

In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integr…