Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

• URL: http://arxiv.org/abs/2603.21309v2
• Date: Tue, 24 Mar 2026 03:00:35 GMT
• Title: Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
• Abstract summary: This paper introduces Cache Personalization (TTA-CaP), a cache-based TTA method that enables cost-effective personalization of vision-language models for video FER.<n>Our experiments show TTA-CaP can outperform state-of-the-art TTA methods under subject-specific and environmental shifts.
• Score: 59.83490704563065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial expression recognition (FER) in videos requires model personalization to capture the considerable variations across subjects. Vision-language models (VLMs) offer strong transfer to downstream tasks through image-text alignment, but their performance can still degrade under inter-subject distribution shifts. Personalizing models using test-time adaptation (TTA) methods can mitigate this challenge. However, most state-of-the-art TTA methods rely on unsupervised parameter optimization, introducing computational overhead that is impractical in many real-world applications. This paper introduces TTA through Cache Personalization (TTA-CaP), a cache-based TTA method that enables cost-effective (gradient-free) personalization of VLMs for video FER. Prior cache-based TTA methods rely solely on dynamic memories that store test samples, which can accumulate errors and drift due to noisy pseudo-labels. TTA-CaP leverages three coordinated caches: a personalized source cache that stores source-domain prototypes, a positive target cache that accumulates reliable subject-specific samples, and a negative target cache that stores low-confidence cases as negative samples to reduce the impact of noisy pseudo-labels. Cache updates and replacement are controlled by a tri-gate mechanism based on temporal stability, confidence, and consistency with the personalized cache. Finally, TTA-CaP refines predictions through fusion of embeddings, yielding refined representations that support temporally stable video-level predictions. Our experiments on three challenging video FER datasets, BioVid, StressID, and BAH, indicate that TTA-CaP can outperform state-of-the-art TTA methods under subject-specific and environmental shifts, while maintaining low computational and memory overhead for real-world deployment.

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