activity
20202023
most citedPAN: Towards Fast Action Recognition via Learning Persistence of Appearance

32 citations · 38 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2023

GeT: Generative Target Structure Debiasing for Domain Adaptation

Can Zhang, Gim Hee Lee

Domain adaptation (DA) aims to transfer knowledge from a fully labeled source to a scarcely labeled or totally unlabeled target under domain shift. Recently, semi-supervised learni…

cs.CV2023

Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Jingyi Wang, Can Zhang, Jinfa Huang +2

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooke…

cs.CV20231 cited

Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs

Jingyi Wang, Jinfa Huang, Can Zhang +1

Dynamic scene graphs generated from video clips could help enhance the semantic visual understanding in a wide range of challenging tasks such as environmental perception, autonomo…

cs.CV20225 cited

Unsupervised Pre-training for Temporal Action Localization Tasks

Can Zhang, Tianyu Yang, Junwu Weng +3

Unsupervised video representation learning has made remarkable achievements in recent years. However, most existing methods are designed and optimized for video classification. The…

cs.CV2021

Long-Short Temporal Modeling for Efficient Action Recognition

Liyu Wu, Yuexian Zou, Can Zhang

Efficient long-short temporal modeling is key for enhancing the performance of action recognition task. In this paper, we propose a new two-stream action recognition network, terme…

cs.CV2021

SRF-Net: Selective Receptive Field Network for Anchor-Free Temporal Action Detection

Ranyu Ning, Can Zhang, Yuexian Zou

Temporal action detection (TAD) is a challenging task which aims to temporally localize and recognize the human action in untrimmed videos. Current mainstream one-stage TAD approac…