activity
20142025
most citedMMDetection: Open MMLab Detection Toolbox and Benchmark

794 citations · 5.6k across the 185 of their papers we have counts for

collaborators

186 papers

cs.CV2025

Model Compression using Progressive Channel Pruning

Jinyang Guo, Weichen Zhang, Wanli Ouyang +1

In this work, we propose a simple but effective channel pruning framework called Progressive Channel Pruning (PCP) to accelerate Convolutional Neural Networks (CNNs). In contrast t…

cs.CV2025

Dense Video Captioning using Graph-based Sentence Summarization

Zhiwang Zhang, Dong Xu, Wanli Ouyang +1

Recently, dense video captioning has made attractive progress in detecting and captioning all events in a long untrimmed video. Despite promising results were achieved, most existi…

cs.CV2025

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization

Zhiwang Zhang, Dong Xu, Wanli Ouyang +1

In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where ea…

cs.CV2025

Progressive Modality Cooperation for Multi-Modality Domain Adaptation

Weichen Zhang, Dong Xu, Jing Zhang +1

In this work, we propose a new generic multi-modality domain adaptation framework called Progressive Modality Cooperation (PMC) to transfer the knowledge learned from the source do…

cs.CV2025

Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation

Weichen Zhang, Dong Xu, Wanli Ouyang +1

This paper proposes a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN), which uses the domain-collaborative and domain-adversarial lea…

cs.CV2025★ 16 cited

Improving Weakly Supervised Temporal Action Localization by Exploiting Multi-resolution Information in Temporal Domain

Rui Su, Dong Xu, Luping Zhou +1

Weakly supervised temporal action localization is a challenging task as only the video-level annotation is available during the training process. To address this problem, we propos…