While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting
Fuzhou University（福州大学）；University of Science and Technology of China（中国科学技术大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Shandong Normal University（山东师范大学）；Sun Yat-sen University（中山大学）
Q-Doc: Benchmarking Document Image Quality Assessment Capabilities in Multi-modal Large Language Models
South China Normal University（华南师范大学）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Shenzhen Academy of Inspection and Quarantine（深圳检验检疫局）；Shenzhen University（深圳大学）
Taiyuan University of Technology（太原理工大学）；University of Science and Technology of China（中国科学技术大学）；Second Hospital of Shanxi Medical University（山西医科大学第二医院）
Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering
KITE Research Institute（KITE研究 institute）；Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）；Faculty of Applied Science and Engineering（应用科学与工程学院）；University of Toronto（多伦多大学）；College of Engineering and Technology（工程与技术学院）；American University of the Middle East（中东美国大学）
IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting
MAIS, Institute of Automation（自动化研究所信息处理中心）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Research Center of Aerospace Information, Institute of Automation（自动化研究所航空信息研究中心）
Hallucinating 360°: Panoramic Street-View Generation via Local Scenes Diffusion and Probabilistic Prompting
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（人工智能与机器人学院和机器人视觉感知与控制技术国家工程研究中心，湖南大学）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（人机学与机器人研究所，卡尔斯鲁厄技术大学）
