RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912985502449664 |
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| author | Tsai, Ting Yu Yu, An Lee, Lucy Ye, Felix X. -F. Shin, Damian S. Kao, Tzu-Jen Li, Xin Chang, Ming-Ching |
| author_facet | Tsai, Ting Yu Yu, An Lee, Lucy Ye, Felix X. -F. Shin, Damian S. Kao, Tzu-Jen Li, Xin Chang, Ming-Ching |
| contents | Animal models, particularly rats, play a critical role in seizure research for studying epileptogenesis and treatment response. However, progress is limited by the lack of datasets with precise temporal annotations and standardized evaluation protocols. Existing animal behavior datasets often have limited accessibility, coarse labeling, and insufficient temporal localization of clinically meaningful events. To address these limitations, we introduce RatSeizure, the first publicly benchmark for fine-grained seizure behavior analysis. The dataset consists of recorded clips annotated with seizure-related action units and temporal boundaries, enabling both behavior classification and temporal localization. We further propose RaSeformer, a saliency-context Transformer for temporal action localization that highlights behavior-relevant context while suppressing redundant cues. Experiments on RatSeizure show that RaSeformer achieves strong performance and provides a competitive reference model for this challenging task. We also establish standardized dataset splits and evaluation protocols to support reproducible benchmarking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26780 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization Tsai, Ting Yu Yu, An Lee, Lucy Ye, Felix X. -F. Shin, Damian S. Kao, Tzu-Jen Li, Xin Chang, Ming-Ching Computer Vision and Pattern Recognition Animal models, particularly rats, play a critical role in seizure research for studying epileptogenesis and treatment response. However, progress is limited by the lack of datasets with precise temporal annotations and standardized evaluation protocols. Existing animal behavior datasets often have limited accessibility, coarse labeling, and insufficient temporal localization of clinically meaningful events. To address these limitations, we introduce RatSeizure, the first publicly benchmark for fine-grained seizure behavior analysis. The dataset consists of recorded clips annotated with seizure-related action units and temporal boundaries, enabling both behavior classification and temporal localization. We further propose RaSeformer, a saliency-context Transformer for temporal action localization that highlights behavior-relevant context while suppressing redundant cues. Experiments on RatSeizure show that RaSeformer achieves strong performance and provides a competitive reference model for this challenging task. We also establish standardized dataset splits and evaluation protocols to support reproducible benchmarking. |
| title | RatSeizure: A Benchmark and Saliency-Context Transformer for Rat Seizure Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26780 |