Action Reinforcement and Indication Module for Single-frame Temporal Action Localization

Single-frame temporal action localization aims to predict the start time, end time, and categories of action instances in untrimmed videos with only one timestamp label for each action instance. However, recent works have two challenges: incomplete location results caused by various same-class action snippets (or frames) and redundant predictions due to ambiguous background snippets. To tackle the above issues, we design a model consisting of an action reinforcement module (ARM) and an action indication module (AIM). Specifically, the ARM improves the model's generalization ability by augmenting local and global features, thus solving the challenges of incomplete predictions. Simultaneously, the AIM detects the frames that indicate the location of the action instances (called keyframes) in the training period. Then the AIM applies the detected keyframes to filter redundant prediction results in the testing period. Extensive experiments are performed on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3, demonstrating that the model could achieve state-of-the-art performance compared to some competitive approaches.