11 citations · 11 across the 3 of their papers we have counts for
5 papers · 1 filter
Unsupervised Domain Adaptation via Similarity-based Prototypes for Cross-Modality Segmentation
Ziyu Ye, Chen Ju, Chaofan Ma +1
Deep learning models have achieved great success on various vision challenges, but a well-trained model would face drastic performance degradation when applied to unseen data. Sinc…
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding
Haicheng Wang, Chen Ju, Weixiong Lin +4
Temporal sentence grounding aims to detect event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves grea…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses
Chen Ju, Peisen Zhao, Ya Zhang +2
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the…
Bottom-Up Temporal Action Localization with Mutual Regularization
Peisen Zhao, Lingxi Xie, Chen Ju +3
Recently, temporal action localization (TAL), i.e., finding specific action segments in untrimmed videos, has attracted increasing attentions of the computer vision community. Stat…