Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
Facial expression recognition (FER) in videos requires model personalization to capture considerable variation across subjects. Vision-language models (VLMs) offer strong transfer through image-text alignment, but their performance can degrade under inter-subject distribution shifts. Test-time adaptation (TTA) can mitigate this challenge, yet most state-of-the-art methods rely on unsupervised parameter optimization, introducing computational overhead that limits real-world deployment. This paper introduces TTA through Cache Personalization (TTA-CaP), a gradient-free, cache-based method for cost-effective personalization of VLMs in video FER. Unlike prior cache-based TTA methods that rely ...
| Authors | Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 March 2026 |
| Last updated at source | 4 September 2026 |
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