Improving action segmentation via explicit similarity measurement
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917924324769792 |
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| author | Aouaidjia, Kamel Zhang, Wenhao Li, Aofan Zhang, Chongsheng |
| author_facet | Aouaidjia, Kamel Zhang, Wenhao Li, Aofan Zhang, Chongsheng |
| contents | Existing supervised action segmentation methods depend on the quality of frame-wise classification using attention mechanisms or temporal convolutions to capture temporal dependencies. Even boundary detection-based methods primarily depend on the accuracy of an initial frame-wise classification, which can overlook precise identification of segments and boundaries in case of low-quality prediction. To address this problem, this paper proposes ASESM (Action Segmentation via Explicit Similarity Measurement) to enhance the segmentation accuracy by incorporating explicit similarity evaluation across frames and predictions. Our supervised learning architecture uses frame-level multi-resolution features as input to multiple Transformer encoders. The resulting multiple frame-wise predictions are used for similarity voting to obtain high quality initial prediction. We apply a newly proposed boundary correction algorithm that operates based on feature similarity between consecutive frames to adjust the boundary locations iteratively through the learning process. The corrected prediction is then further refined through multiple stages of temporal convolutions. As post-processing, we optionally apply boundary correction again followed by a segment smoothing method that removes outlier classes within segments using similarity measurement between consecutive predictions. Additionally, we propose a fully unsupervised boundary detection-correction algorithm that identifies segment boundaries based solely on feature similarity without any training. Experiments on 50Salads, GTEA, and Breakfast datasets show the effectiveness of both the supervised and unsupervised algorithms. Code and models are made available on Github. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving action segmentation via explicit similarity measurement Aouaidjia, Kamel Zhang, Wenhao Li, Aofan Zhang, Chongsheng Computer Vision and Pattern Recognition Existing supervised action segmentation methods depend on the quality of frame-wise classification using attention mechanisms or temporal convolutions to capture temporal dependencies. Even boundary detection-based methods primarily depend on the accuracy of an initial frame-wise classification, which can overlook precise identification of segments and boundaries in case of low-quality prediction. To address this problem, this paper proposes ASESM (Action Segmentation via Explicit Similarity Measurement) to enhance the segmentation accuracy by incorporating explicit similarity evaluation across frames and predictions. Our supervised learning architecture uses frame-level multi-resolution features as input to multiple Transformer encoders. The resulting multiple frame-wise predictions are used for similarity voting to obtain high quality initial prediction. We apply a newly proposed boundary correction algorithm that operates based on feature similarity between consecutive frames to adjust the boundary locations iteratively through the learning process. The corrected prediction is then further refined through multiple stages of temporal convolutions. As post-processing, we optionally apply boundary correction again followed by a segment smoothing method that removes outlier classes within segments using similarity measurement between consecutive predictions. Additionally, we propose a fully unsupervised boundary detection-correction algorithm that identifies segment boundaries based solely on feature similarity without any training. Experiments on 50Salads, GTEA, and Breakfast datasets show the effectiveness of both the supervised and unsupervised algorithms. Code and models are made available on Github. |
| title | Improving action segmentation via explicit similarity measurement |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.10713 |