Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization

TL;DR

This paper introduces a probabilistic vision-language framework for weakly supervised temporal action localization, effectively aligning human action and VLP knowledge in a joint space to improve detection accuracy.

Contribution

It proposes a novel probabilistic embedding space and contrastive learning methods to better capture fine-grained human motions and align action knowledge with VLP.

Findings

Outperforms previous state-of-the-art methods

Significant improvement in localization accuracy

Effective alignment of action and VLP knowledge

Abstract

Weakly supervised temporal action localization (WTAL) aims to detect action instances in untrimmed videos using only video-level annotations. Since 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 through 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 a joint space. Furthermore, the deterministic representation employed in previous studies struggles to capture fine-grained human motions. To address these problems, we propose a novel framework that aligns human action knowledge and VLP knowledge in a 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. Code is available at https://github.com/sejong-rcv/PVLR.