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

• URL: http://arxiv.org/abs/2403.19225v1
• Date: Thu, 28 Mar 2024 08:39:44 GMT
• Title: Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment
• Authors: Angchi Xu, Wei-Shi Zheng,
• Abstract summary: Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts. Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript. We propose a novel Action-Transition-Aware Boundary Alignment framework to efficiently and effectively filter out noisy boundaries and detect transitions.
• Score: 33.74853437611066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed.

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