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Mubarak Shah

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#artificial intelligence Preprint Oct 2026

CineMR: Tool-Integrated Vision-Language Reasoning for Quantitative Cardiac MRI Assessment

CineMR is presented, a tool-augmented VLM that invokes cardiac image-analysis tools and integrates their outputs into interleaved reasoning for quantitative CMR assessment and a multi-cohort visual question answering benchmark covering quantitative metric extraction, multiclass diagnosis, and differential diagnosis.

Kun-Yang Li, H. Nguyen, Joshua Lowe et al. · 0 citations
Preprint Aug 2026

On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation

Text-to-image (T2I) generation has achieved remarkable progress in recent years. However, existing research has largely focused on English-only settings, leaving cross-lingual performance gaps and language-specific effects insufficiently explored. To fill this gap, we introduce LingT2I, a benchmark covering 10 widely u...

Si-Cheng Zhang, Zhong-Hao Yan, Bin-Zhu Xie et al. · 0 citations
#computer vision Sep 2025

The Telephone Game: Evaluating Semantic Drift in Unified Models

Mean Cumulative Drift (MCD), an embedding-based measure of content retention across three representation spaces, and Multi-Generation GenEval (MGG), extending GenEval's object-level compliance scoring across generations are proposed, to quantify drift.

Sabbir Mollah, Rohit Gupta, Sirnam Swetha et al. · 4 citations

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