MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
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Abstract (arXiv)
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. While traditional approaches follow a two-step pipeline, by generating frame-wise probabilities and then feeding them to high-level temporal models, recent approaches use temporal convolutions to directly classify the video frames. In this paper, we introduce a multi-stage architecture for the temporal action segmentation task. Each stage features a set of dilated temporal convolutions to generate an initial prediction that is refined by the next one. This architecture is trained using a combination of a classification loss and a proposed smoothing loss that penalizes over-segmentation errors. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.
Key facts. Cited by 370 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2019.
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Citing papers per year: 2021: 49; 2022: 65; 2023: 65; 2024: 60; 2025: 71; 2026: 60.
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