AI RESEARCH
Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
arXiv CS.CV
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ArXi:2603.21309v1 Announce Type: new 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,