Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability. However, AI-generated Images (AIGIs) may not satisfy human preferences due to their unique distortions, which highlights the necessity to understand and evaluate human preferences for AIGIs. To this end, in this paper, we first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+, which provides human visual preference scores and detailed preference explanations from three perspectives including quality, authenticity, and correspondence. Then, based on the constructed AIGCIQA2023+ database, this paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning. Specifically, the MINT-IQA model first learn and evaluate human preferences for AI-generated Images from multi-perspectives, then via the vision-language instruction tuning strategy, MINT-IQA attains powerful understanding and explanation ability for human visual preference on AIGIs, which can be used for feedback to further improve the assessment capabilities. Extensive experimental results demonstrate that the proposed MINT-IQA model achieves state-of-the-art performance in understanding and evaluating human visual preferences for AIGIs, and the proposed model also achieves competing results on traditional IQA tasks compared with state-of-the-art IQA models. The AIGCIQA2023+ database and MINT-IQA model are available at: https://github.com/IntMeGroup/MINT-IQA.
Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Department of Electrical and Computer Engineering, The University of British Columbia（英属哥伦比亚大学电气与计算机工程系）；Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院机器人与机电工程系）；Division of Intelligent Robot, Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院智能机器人系）；Vector Institute（向量研究所）；Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH)（釜山科学技术大学计算机科学与工程系）
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Auckland（奥克兰大学）；National University of Singapore（新加坡国立大学）
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity
Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Tsinghua University（清华大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）；University of Electronic Science and Technology of China（电子科技大学）；Tongji University（同济大学）
Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Shenzhen Polytechnic University（深圳职业技术大学）；ZTE Corporation（中兴通讯股份有限公司）；Peng Cheng Laboratory（鹏城实验室）
TraMP-LLaMA: Generative Interpretability with Decoupled Instruction Tuning for Facial Expression Quality Assessment
School of Computer Science University of Bristol（布里斯托大学计算机科学学院）；Translational Health Sciences University of Bristol（布里斯托大学转化健康科学学院）；School of Computing and Communications Lancaster University（兰卡斯特大学计算与通信学院）
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；Alibaba Group（阿里巴巴集团）
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；Meituan Group（美团集团）
Continual Instruction Tuning for Large Multimodal Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Stanford University（斯坦福大学）；Emory University（埃默里大学）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
