activity
20182022
most citedMVT: Mask Vision Transformer for Facial Expression Recognition in the wild

48 citations · 98 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2022

ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation

Jinze Chen, Yang Wang, Yang Cao +2

Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitor…

cs.CV20212 cited

Disentangle Your Dense Object Detector

Zehui Chen, Chenhongyi Yang, Qiaofei Li +3

Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding.…

cs.CV202148 cited

MVT: Mask Vision Transformer for Facial Expression Recognition in the wild

Hanting Li, Mingzhe Sui, Feng Zhao +2

Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of…

cs.CV2021

Tracking by Joint Local and Global Search: A Target-aware Attention based Approach

Xiao Wang, Jin Tang, Bin Luo +3

Tracking-by-detection is a very popular framework for single object tracking which attempts to search the target object within a local search window for each frame. Although such l…

cs.CV202132 cited

Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer

Yulin Li, Jianfeng He, Tianzhu Zhang +3

Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To addre…

cs.CV2021

Action Unit Memory Network for Weakly Supervised Temporal Action Localization

Wang Luo, Tianzhu Zhang, Wenfei Yang +4

Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without frame-level an…