Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting
Keywords: micro-expression spotting, semi-supervised learning, soft pseudo-labeling
TL;DR: An instance-adaptive Gaussian-based soft pseudo-labeling method for point-supervised facial expression spotting, which models the expression intensity distribution at the instance level.
Abstract: Point-supervised facial expression spotting (P-FES) aims to localize facial expression instances in untrimmed videos, requiring only a single timestamp label for each instance during training. To address label sparsity, hard pseudo-labeling is often employed to propagate point labels to unlabeled frames; however, this approach can lead to confusion when distinguishing between neutral and expression frames with various intensities, which can negatively impact model performance. In this paper, we propose a two-branch framework for P-FES that incorporates a Gaussian-based instance-adaptive Intensity Modeling (GIM) module for soft pseudo-labeling. GIM models the expression intensity distribution for each instance. Specifically, we detect the pseudo-apex frame around each point label, estimate the duration, and construct a Gaussian distribution for each expression instance. We then assign soft pseudo-labels to pseudo-expression frames as intensity values based on the Gaussian distribution. Additionally, we introduce an Intensity-Aware Contrastive (IAC) loss to enhance discriminative feature learning and suppress neutral noise by contrasting neutral frames with expression frames of various intensities. Extensive experiments on the SAMM-LV and CAS(ME)$^2$ datasets demonstrate the effectiveness of our proposed framework.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1490
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