SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting

[2407.20799] SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2407.20799 (cs) [Submitted on 30 Jul 2024 ( v1 ), last revised 11 Apr 2026 (this version, v3)] Title: SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting Authors: Yicheng Deng , Hideaki Hayashi , Hajime Nagahara View a PDF of the paper titled SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting, by Yicheng Deng and 2 other authors View PDF HTML (experimental) Abstract: Facial expression spotting, identifying periods where facial expressions occur in a video, is a significant yet challenging task in facial expression analysis. The issues of irrelevant facial movements and the challenge of detecting subtle motions in micro-expressions remain unresolved, hindering accurate expression spotting. In this paper, we propose an efficient framework for facial expression spotting. First, we propose a Compact Sliding-Window-based Multi-temporal-Resolution Optical flow (CSW-MRO) feature, which calculates multi-temporal-resolution optical flow of the input image sequence within compact sliding windows. The window length is tailored to perceive complete micro-expressions and distinguish between general macro- and micro-expressions. CSW-MRO can effectively reveal subtle motions while avoiding the optical flow being dominated by head movements. Second, we propose SpotFormer, a multi-scale spatio-temporal Transformer that simultaneously encodes spatio-temporal relationships of the CSW-MRO features for accurate frame-level probability estimation. In SpotFormer, we use the proposed Facial Local Graph Pooling (FLGP) operation and convolutional layers to extract multi-scale spatio-temporal features. We show the validity of the architecture of SpotFormer by comparing it with several model variants. Third, we introduce supervised contrastive learning into SpotFormer to enhance the discriminability between different types of expressions. Extensive experiments on SAMM-LV, CAS(ME)^2, and CAS(ME)^3 show that our method outperforms state-of-the-art models, particularly in micro-expression spotting. Code is available at this https URL . Comments: Accepted by IEEE Transactions on Affective Computing Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2407.20799 [cs.CV] (or arXiv:2407.20799v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2407.20799 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yicheng Deng [ view email ] [v1] Tue, 30 Jul 2024 13:02:08 UTC (3,708 KB) [v2] Fri, 21 Nov 2025 05:12:45 UTC (2,760 KB) [v3] Sat, 11 Apr 2026 11:32:41 UTC (2,839 KB) Full-text links: Access Paper: View a PDF of the paper titled SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting, by Yicheng Deng and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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