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.
A systematic evaluation of CL for MedVQA across diverse clinical objectives, including classification, multi-label classification, detection, cell counting, and report generation suggests that existing CL methods struggle to maintain stability-plasticity balance when tasks with different objectives and supervision form...
Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed et al.· arXiv.org· 0 citations
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