most citedLearning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition

8 citations · 21 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20225 cited

Human-Object Interaction Detection via Disentangled Transformer

Desen Zhou, Zhichao Liu, Jian Wang +4

Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions. Existing HOI transformers either adopt a single decode…

cs.CV20228 cited

Implicit Sample Extension for Unsupervised Person Re-Identification

Xinyu Zhang, Dongdong Li, Zhigang Wang +5

Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different…

cs.CV2022

MixFormer: Mixing Features across Windows and Dimensions

Qiang Chen, Qiman Wu, Jian Wang +5

While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs…

cs.CV20218 cited

Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition

Tailin Chen, Desen Zhou, Jian Wang +4

The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion. E…

cs.CV2021

Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

Zechen Bai, Zhigang Wang, Jian Wang +2

Unsupervised domain adaptation (UDA) methods for person re-identification (re-ID) aim at transferring re-ID knowledge from labeled source data to unlabeled target data. Although ac…