Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition artifacts in endoscopy remains insufficiently characterized. In practice, degradations such as defocus, haze, motion blur, noise, cautery smoke,...
Darakshan Rashid, Raza Imam, Ufaq Khan et al.· 0 citations
Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchmarks present the complete image and question at once. We ask what models lose when the same information is fragmented. We introduce Layered-VQA, with 93 scenes and 300...
L. D. M. S. Sai Teja, Ufaq Khan, N. S. G. Krishna et al.· 0 citations
The underlying causes of miscalibration in VLMs and Medical-VLMs are investigated and a simple train-time calibration method, Multi-Class Margin (MCM) regularization, is proposed, which achieves lowest ECE on 10 out of 12 settings in in-domain and remains competitive under domain shifts.
DCRM-ViT is proposed, a domain-conditioned residual modulation framework for Vision Transformers that preserves general-vision knowledge while adapting to diverse medical and natural domains and achieves strong cross-domain performance with low overhead.
Ufaq Khan, Umair Nawaz, Massimo Caputo et al.· 0 citations
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