4 papers
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach
Qinying Liu, Zilei Wang, Shenghai Rong +2
Weakly-supervised temporal action localization aims to localize action instances in videos with only video-level action labels. Existing methods mainly embrace a localization-by-cl…
TagAlign: Improving Vision-Language Alignment with Multi-Tag Classification
Qinying Liu, Wei Wu, Kecheng Zheng +6
The crux of learning vision-language models is to extract semantically aligned information from visual and linguistic data. Existing attempts usually face the problem of coarse ali…
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition
Qinying Liu, Zilei Wang
In this work, we consider the problem of cross-domain 3D action recognition in the open-set setting, which has been rarely explored before. Specifically, there is a source domain a…
Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization
Qinying Liu, Zilei Wang, Ruoxi Chen +1
Weakly-supervised temporal action localization (WTAL) intends to detect action instances with only weak supervision, e.g., video-level labels. The current~\textit{de facto} pipelin…