@inproceedings{
lim2024probabilistic,
title={Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization},
author={GeunTaek Lim and Hyunwoo Kim and Joonsoo Kim and Yukyung Choi},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=N0ZsDei5PV}
}
Weakly supervised temporal action localization (WTAL) aims to detect action instances in untrimmed videos with only video-level annotations. As many existing works optimize WTAL models based on action classification labels, they encounter the task discrepancy problem (i.e., localization-by-classification). To tackle this issue, recent studies have attempted to utilize action category names as auxiliary semantic knowledge with vision-language pre-training (VLP). However, there are still areas where existing research falls short. Previous approaches primarily focused on leveraging textual information from language models but overlooked the alignment of dynamic human action and VLP knowledge in joint space. Furthermore, the deterministic representation employed in previous studies struggles to capture fine-grained human motion. To address these problems, we propose a novel framework that aligns human action knowledge and VLP knowledge in the probabilistic embedding space. Moreover, we propose intra- and inter-distribution contrastive learning to enhance the probabilistic embedding space based on statistical similarities. Extensive experiments and ablation studies reveal that our method significantly outperforms all previous state-of-the-art methods. Our code will be available after publication.
Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization
GeunTaek Lim Hyunwoo Kim Joonsoo Kim Yukyung Choi