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20232026
most citedExploring the Naturalness of AI-Generated Images

9 citations · 29 across the 33 of their papers we have counts for

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cs.CV2026

GeoR-Bench: Evaluating Geoscience Visual Reasoning

Yushuo Zheng, Zicheng Zhang, Huiyu Duan +7

Geoscience intelligence is expected to understand, reason about, and predict earth system changes to support human decision-making in critical domains such as disaster response, cl…

cs.CV2026

A: Towards Advertising Aesthetic Assessment

Kaiyuan Ji, Yixuan Gao, Lu Sun +7

Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized…

cs.CV2026

KidVis: Do Multimodal Large Language Models Possess the Visual Perceptual Capabilities of a 6-Year-Old?

Xianfeng Wang, Kaiwei Zhang, Qi Jia +3

While Multimodal Large Language Models (MLLMs) have demonstrated impressive proficiency in high-level reasoning tasks, such as complex diagrammatic interpretation, it remains an op…

cs.CV2025

VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment

Ziheng Jia, Linhan Cao, Jinliang Han +6

Developing a robust visual quality assessment (VQualA) large multi-modal model (LMM) requires achieving versatility, powerfulness, and transferability. However, existing VQualA LMM…

cs.CV2025

MACEval: A Multi-Agent Continual Evaluation Network for Large Models

Zijian Chen, Yuze Sun, Yuan Tian +2

Hundreds of benchmarks dedicated to evaluating large models have been presented over the past few years. However, most of them remain closed-ended and are prone to overfitting due…

cs.CV2025

Refine-IQA: Multi-Stage Reinforcement Finetuning for Perceptual Image Quality Assessment

Ziheng Jia, Jiaying Qian, Zicheng Zhang +2

Reinforcement fine-tuning (RFT) is a proliferating paradigm for LMM training. Analogous to high-level reasoning tasks, RFT is similarly applicable to low-level vision domains, incl…