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VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models
Yunhao Li, Sijing Wu, Zhilin Gao +5
Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perception tasks, which has in turn made the evaluation of LMMs significant. However, th…
Q-Bench-Portrait: Benchmarking Multimodal Large Language Models on Portrait Image Quality Perception
Sijing Wu, Yunhao Li, Zicheng Zhang +5
Recent advances in multimodal large language models (MLLMs) have demonstrated impressive performance on existing low-level vision benchmarks, which primarily focus on generic image…
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…
Exploring Instruction Data Quality for Explainable Image Quality Assessment
Yunhao Li, Sijing Wu, Jun Jia +5
In recent years, with the rapid development of large multimodal models (LMMs), explainable image quality assessment (IQA) has attracted increasing attention, aiming to understand t…
Can Large Models Fool the Eye? A New Turing Test for Biological Animation
Zijian Chen, Lirong Deng, Zhengyu Chen +5
Evaluating the abilities of large models and manifesting their gaps are challenging. Current benchmarks adopt either ground-truth-based score-form evaluation on static datasets or…