Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment

The core message of this work is to directly localize action transitions for efficient pseudo segmentation generation during training, without the need of time-consuming frame-by-frame alignment. A novel Action-Transition-Aware Boundary Alignment (ATBA) framework is proposed to efficiently and effectively filter out noisy boundaries and detect transitions, and video-level losses are introduced to improve the semantic robustness.