Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substantial computational costs in both training and inference, limiting accessibility to the broader research and user communities. A straightforward solution is to leverage smaller pre-trained vision and language models, which inevitably cause significant performance drops. In this paper, we demonstrate the possibility of training a smaller but better MLLM with high-quality training data. Specifically, we introduce Bunny, a family of lightweight MLLMs with flexible vision and language backbones for efficient multimodal learning from selected training data. Experiments show that our Bunny-4B/8B outperforms the state-of-the-art large MLLMs on multiple benchmarks. We expect that this work can provide the community with a clean and flexible open-source tool for further research and development. The code, models, and data can be found in https://github.com/BAAI-DCAI/Bunny.
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
Vision-Centric Activation and Coordination for Multimodal Large Language Models
MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University（人工智能MoE实验室，上海交通大学）；Ant Group（蚂蚁集团）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo（宁波数字孪生研究所，东部技术研究所，宁波）
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Fudan University（复旦大学）；Meituan（美团）；Shanghai Key Lab of Intell. Info. Processing（上海智能信息处理重点实验室）；Shanghai Collaborative Innovation Center on Intelligent Visual Computing（上海智能视觉计算协同创新中心）
Player-Centric Multimodal Prompt Generation for Large Language Model Based Identity-Aware Basketball Video Captioning
Beijing University of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；Chinese Academy of Sciences（中国科学院）；The Hong Kong Polytechnic University（香港理工大学）
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Hong Kong University of Science and Technology（香港科技大学）；Apple（苹果公司）；Peking University（北京大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Science and Technology of China（中国科学技术大学）
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution
Nanjing University（南京大学）；LLM Team, Shopee Pte. Ltd.（Shopee 联邦学习团队）；East China Normal University（华东师范大学）；Nanjing University of Science and Technology（南京理工大学）；Central South University（中南大学）
MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Shanghai Artificial Intelligence Laboratory, OpenDataLab（上海人工智能实验室、OpenDataLab）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；The University of Hong Kong（香港大学）
Closing the Gap: Data-Centric Fine-Tuning of Vision Language Models for the Standardized Exam Questions
organization= Department of Computer Engineering, Middle East Technical University (METU) , city= Ankara , country= Türkiye；organization= METU-DTX Digital Transformation \& Innovation Centre, METU , city= Ankara , country= Türkiye
ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
DAMO Academy, Alibaba Group（达摩院，阿里巴巴集团）；Hupan Laboratory（湖畔实验室）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Radiology, The Affiliated Yangming Hospital of Ningbo University（宁波大学附属阳明医院放射科）；Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院，浙江大学）；Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University（清华长庚医院肝胆胰中心，清华大学临床医学院，清华医学，清华大学）；School of Software, Tsinghua University（清华大学软件学院）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
College of Computer Science, Sichuan University（四川大学计算机科学学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Technical University of Munich, Munich Center for Machine Learning (MCML), MDSI（慕尼黑工业大学，慕尼黑机器学习中心（MCML），MDSI）；LTCI, Télécom Paris, Institut Polytechnique de Paris, France（法国巴黎理工学院，巴黎电信学院，LTCI）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University of California, Berkeley（加州大学伯克利分校）
Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation
S-Lab, Nanyang Technological University（南洋理工大学S-Lab）；SenseTime Research（商汤科技研究院）；University of Michigan（密歇根大学）；Max-Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
Urban Boundaries, Social Barriers: A Benchmark and Vision-Centric Framework for Mapping Gated Communities and Equity Implications
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Public Administration and Policy, Renmin University of China（中国人民大学公共管理学院）；Sony AI（索尼人工智能）；University of South China（南华大学）
