Multi-Stage Temporal Convolutional Network For Action Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:1903.01945 (cs) [Submitted on 5 Mar 2019 (v1), last revised 2 Apr 2019 (this version, v2)] Title:MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation Authors:Yazan Abu Farha, Juergen Gall View a PDF of the paper titled MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation, by Yazan Abu Farha and Juergen Gall View PDF

Abstract: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.

Comments:	CVPR 2019 Camera Ready
Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:1903.01945 [cs.CV]
(or arXiv:1903.01945v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.1903.01945 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Yazan Abu Farha [view email] [v1] Tue, 5 Mar 2019 17:29:37 UTC (1,944 KB) [v2] Tue, 2 Apr 2019 15:35:40 UTC (1,747 KB) Full-text links:

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