Existing approaches to action segmentation use pre-computed frame features extracted by methods which have been trained on tasks that are different from action segmentation. Also, recent approaches typically use deep framewise representations that lack explicit modeling of action segments. To address these shortcomings, we introduce the first end-to-end solution to action segmentation -- End-to-End Action Segmentation Transformer (EAST). Our key contributions include: (1) a simple and efficient adapter design for effective backbone fine-tuning; (2) a segmentation-by-detection framework for leveraging action proposals initially predicted over a coarsely downsampled video toward labeling of all frames; and (3) a new action-proposal based data augmentation for robust training. EAST achieves state-of-the-art performance on standard benchmarks, including GTEA, 50Salads, Breakfast, and Assembly-101. The model and corresponding code will be released.
View on arXiv@article{wang2025_2503.06316,
title={ End-to-End Action Segmentation Transformer },
author={ Tieqiao Wang and Sinisa Todorovic },
journal={arXiv preprint arXiv:2503.06316},
year={ 2025 }
}