Efficient Temporal Action Segmentation via Boundary-aware Query Voting

• URL: http://arxiv.org/abs/2405.15995v1
• Date: Sat, 25 May 2024 00:44:13 GMT
• Title: Efficient Temporal Action Segmentation via Boundary-aware Query Voting
• Authors: Peiyao Wang, Yuewei Lin, Erik Blasch, Jie Wei, Haibin Ling,
• Abstract summary: BaFormer is a boundary-aware Transformer network that tokenizes each video segment as an instance token. BaFormer significantly reduces the computational costs, utilizing only 6% of the running time.
• Score: 51.92693641176378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the performance of Temporal Action Segmentation (TAS) has improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements. To improve the efficiency while keeping the performance, we present a novel perspective centered on per-segment classification. By harnessing the capabilities of Transformers, we tokenize each video segment as an instance token, endowed with intrinsic instance segmentation. To realize efficient action segmentation, we introduce BaFormer, a boundary-aware Transformer network. It employs instance queries for instance segmentation and a global query for class-agnostic boundary prediction, yielding continuous segment proposals. During inference, BaFormer employs a simple yet effective voting strategy to classify boundary-wise segments based on instance segmentation. Remarkably, as a single-stage approach, BaFormer significantly reduces the computational costs, utilizing only 6% of the running time compared to state-of-the-art method DiffAct, while producing better or comparable accuracy over several popular benchmarks. The code for this project is publicly available at https://github.com/peiyao-w/BaFormer.

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