Action-Aware Masking Network With Group-Based Attention for Temporal Action Localization

Tae-Kyung Kang, Gun-Hee Lee, Kyung-Min Jin, Seong-Whan Lee; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 6058-6067

Temporal Action Localization (TAL) is a significant and challenging task that searches for subtle human activities in an untrimmed video. To extract snippet-level video features, existing TAL methods commonly use video encoders pre-trained on short-video classification datasets. However, the snippet-level features can incur ambiguity between consecutive frames due to short and poor temporal information, disrupting the precise prediction of action instances. Several methods incorporating temporal relations have been proposed to mitigate this problem; however, they still suffer from poor video features. To address this issue, we propose a novel temporal action localization framework called an Action-aware Masking Network (AMNet). Our method simultaneously refines video features using action-aware attention and considers inherent temporal relations using self-attention and cross-attention mechanisms. First, we present an Action Masking Encoder (AME) that generates an action-aware mask to represent positive characteristics, which is then used to refine snippet-level features to be more salient around actions. Second, we design a Group Attention Module (GAM), which models relations of temporal information and exchanges mutual information by dividing the features into two groups, i.e., long and short-groups. Extensive experiments and ablation studies on two primary benchmark datasets demonstrate the effectiveness of AMNet, and our method achieves state-of-the-art performances on THUMOS-14 and ActivityNet1.3.