Jia-Qi Lin

Learning Well-Structured Logits: Leveraging Vision–Language Complementarity for Open-World Test-Time Adaptation

Open-world test-time adaptation (OWTTA) is increasingly studied for its ability to adapt models at inference time in the presence of both domain discrepancy and semantic variance. Existing methods typically rely on either discriminative models or vision-language models (VLMs) alone, leaving their co