Publication
2by2: weakly-supervised learning for global action segmentation
Conference Article
Conference
International Conference on Pattern Recognition (ICPR)
Edition
27th
Pages
380-395
Doc link
https://doi.org/10.1007/978-3-031-78125-4_26
File
Authors
Projects associated
Abstract
This paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across videos of different activities. Unlike the case of videos depicting all the same activity, the temporal order of actions is not roughly shared among all videos, making the task even more challenging. We propose to use activity labels to learn, in a weakly-supervised fashion, action representations suitable for global action segmentation. For this purpose, we introduce a triadic learning approach for video pairs, to ensure intra-video action discrimination, as well as inter-video and inter-activity action association. For the backbone architecture, we use a Siamese network based on sparse transformers that takes as input video pairs and determine whether they belong to the same activity. The proposed approach is validated on two challenging benchmark datasets: Breakfast and YouTube Instructions, outperforming state-of-the-art methods.
Categories
pattern recognition.
Author keywords
Temporal Action Segmentation; Weakly-Supervised Learning; Video Alignment.
Scientific reference
E.B. Bueno Benito and M. Dimiccoli. 2by2: weakly-supervised learning for global action segmentation, 27th International Conference on Pattern Recognition, 2024, Kolkata, in Pattern Recognition, Vol 15315 of Lecture Notes in Computer Science, pp. 380-395, 2024, Cham.
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