Author

Seung-Hun Kang

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#computer vision Preprint Oct 2026

Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts

Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adaptation (TTA) offers a practical remedy, yet most existing VLM-TTA methods follow a prediction-side adaptation paradigm. They use test samples to adjust logits, prototypes...

Seungmin Oh, Seung-Hun Kang, Jongbin Ryu · 0 citations

#artificial intelligence Preprint Sep 2026

Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models

This work addresses limitations in transfer learning for vision-language models through transformation-aware prompt conditioning and a re-calibrated contrastive loss, and treats same-class samples as positives rather than distinct instances, enabling the model to learn domain-specific features more effectively.

Seungmin Oh, Seung-Hun Kang, Jongbin Ryu · 0 citations