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cs.CV2026
Evaluating Image Editing with LLMs: A Comprehensive Benchmark and Intermediate-Layer Probing Approach
Shiqi Gao, Zitong Xu, Kang Fu +3
Evaluating text-guided image editing (TIE) methods remains a challenging problem, as reliable assessment should simultaneously consider perceptual quality, alignment with textual i…
cs.CV2026
Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation
Kang Fu, Huiyu Duan, Zicheng Zhang +5
Large Multimodal Models (LMMs) have recently shown remarkable promise in low-level visual perception tasks, particularly in Image Quality Assessment (IQA), demonstrating strong zer…