3 papers
cs.CV2026
Towards Calibrating Prompt Tuning of Vision-Language Models
Ashshak Sharifdeen, Fahad Shamshad, Muhammad Akhtar Munir +6
Prompt tuning of large-scale vision-language models such as CLIP enables efficient task adaptation without updating model weights. However, it often leads to poor confidence calibr…
cs.CV2025
Calibration-Aware Prompt Learning for Medical Vision-Language Models
Abhishek Basu, Fahad Shamshad, Ashshak Sharifdeen +2
Medical Vision-Language Models (Med-VLMs) have demonstrated remarkable performance across diverse medical imaging tasks by leveraging large-scale image-text pretraining. However, t…
cs.CV2025
O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models
Ashshak Sharifdeen, Muhammad Akhtar Munir, Sanoojan Baliah +2
Test-time prompt tuning for vision-language models (VLMs) is getting attention because of their ability to learn with unlabeled data without fine-tuning. Although test-time prompt…