32 citations · 71 across the 13 of their papers we have counts for
7 papers · 1 filter
On Pursuit of Designing Multi-modal Transformer for Video Grounding
Meng Cao, Long Chen, Mike Zheng Shou +2
Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two framework…
Deep Motion Prior for Weakly-Supervised Temporal Action Localization
Meng Cao, Can Zhang, Long Chen +2
Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize actions in untrimmed videos with only video-level labels. Currently, most state-of-the-art WSTAL methods fol…
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…
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…
All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection
Meng Cao, Can Zhang, Dongming Yang +1
Arbitrary-shaped text detection is a challenging task since curved texts in the wild are of the complex geometric layouts. Existing mainstream methods follow the instance segmentat…
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Dongming Yang, Yuexian Zou, Can Zhang +2
Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects. Latest end-to-end HOI detectors are short of relation reasoning, which leads…
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
Can Zhang, Meng Cao, Dongming Yang +2
Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by…