3 citations · 3 across the 14 of their papers we have counts for
4 papers · 1 filter
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propos…
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani +3
Foundation models like CLIP and SAM have advanced computer vision and medical imaging via low-shot transfer learning, aiding CADD with limited data. However, their deployment faces…
Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models
Behraj Khan, Rizwan Qureshi, Nouman Muhammad Durrani +1
Since the establishment of vision-language foundation models as the new mainstay in low-shot vision classification tasks, the question of domain generalization arising from insuffi…
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Behraj Khan, Tahir Syed
Confidence calibration is an emerging challenge in real-world decision systems based on foundations models when used for downstream vision classification tasks. Due to various reas…