activity
20172023
most citedA Comprehensive Study of Deep Video Action Recognition

115 citations · 216 across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.CV2021★ 1 cited

Video Contrastive Learning with Global Context

Haofei Kuang, Yi Zhu, Zhi Zhang +5

Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastiv…

cs.CV2021★ 11 cited

Long Short-Term Transformer for Online Action Detection

Mingze Xu, Yuanjun Xiong, Hao Chen +4

We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged s…

cs.CV2021★ 5 cited

SiamMOT: Siamese Multi-Object Tracking

Bing Shuai, Andrew Berneshawi, Xinyu Li +2

In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. Si…

cs.CV2021★ 3 cited

SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation

Zhe Wang, Hao Chen, Xinyu Li +4

Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos…

cs.CV2021★ 4 cited

VidTr: Video Transformer Without Convolutions

Yanyi Zhang, Xinyu Li, Chunhui Liu +6

We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal infor…

cs.CV2021

TubeR: Tubelet Transformer for Video Action Detection

Jiaojiao Zhao, Yanyi Zhang, Xinyu Li +10

We propose TubeR: a simple solution for spatio-temporal video action detection. Different from existing methods that depend on either an off-line actor detector or hand-designed ac…

cs.CV2021★ 1 cited

Selective Feature Compression for Efficient Activity Recognition Inference

Chunhui Liu, Xinyu Li, Hao Chen +2

Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world appli…