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Research questionHow can video facial-expression recognition personalize vision-language models under shifts without costly test-time optimization?People express emotions differently, and recording conditions can vary across subjects, causing models trained on other data to lose accuracy. Updating model parameters during testing can also impose computational overhead that limits practical use.
AI
Computer Vision
Image & Video Processing
Inference Optimization
Machine Learning
Multimodal Models
Latest papersRecent research connected to this question, newest first.Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in VideosThe source concerns vision-language models for video facial-expression recognition and reports experiments on BioVid, StressID, and BAH under subject-specific and environmental shifts. It provides evidence about recognition performance, computational cost, and memory overhead for test-time adaptation.research paper · Sep 4, 2026
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