Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting

• URL: http://arxiv.org/abs/2403.14240v1
• Date: Thu, 21 Mar 2024 09:01:21 GMT
• Title: Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting
• Authors: Wang-Wang Yu, Xian-Shi Zhang, Fu-Ya Luo, Yijun Cao, Kai-Fu Yang, Hong-Mei Yan, Yong-Jie Li,
• Abstract summary: We propose a point-level weakly-supervised expression spotting framework, where each expression requires to be annotated with only one random frame (i.e., a point) We show MPLG generates more reliable pseudo labels by merging class-specific probabilities, attention scores, fused features, and point-level labels. Experiments on the CAS(ME)2, CAS(ME)3, and SAMM-LV datasets demonstrate PWES achieves promising performance comparable to that of recent fully-supervised methods.
• Score: 22.04975008531069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient information about the location and number of expressions during training, resulting in significantly inferior performance compared with fully-supervised spotting. To bridge this gap, we propose a point-level weakly-supervised expression spotting (PWES) framework, where each expression requires to be annotated with only one random frame (i.e., a point). To mitigate the issue of sparse label distribution, the prevailing solution is pseudo-label mining, which, however, introduces new problems: localizing contextual background snippets results in inaccurate boundaries and discarding foreground snippets leads to fragmentary predictions. Therefore, we design the strategies of multi-refined pseudo label generation (MPLG) and distribution-guided feature contrastive learning (DFCL) to address these problems. Specifically, MPLG generates more reliable pseudo labels by merging class-specific probabilities, attention scores, fused features, and point-level labels. DFCL is utilized to enhance feature similarity for the same categories and feature variability for different categories while capturing global representations across the entire datasets. Extensive experiments on the CAS(ME)^2, CAS(ME)^3, and SAMM-LV datasets demonstrate PWES achieves promising performance comparable to that of recent fully-supervised methods.

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