We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL introduces the Naive Dynamic Resolution mechanism, which enables the model to dynamically process images of varying resolutions into different numbers of visual tokens. This approach allows the model to generate more efficient and accurate visual representations, closely aligning with human perceptual processes. The model also integrates Multimodal Rotary Position Embedding (M-RoPE), facilitating the effective fusion of positional information across text, images, and videos. We employ a unified paradigm for processing both images and videos, enhancing the model's visual perception capabilities. To explore the potential of large multimodal models, Qwen2-VL investigates the scaling laws for large vision-language models (LVLMs). By scaling both the model size-with versions at 2B, 8B, and 72B parameters-and the amount of training data, the Qwen2-VL Series achieves highly competitive performance. Notably, the Qwen2-VL-72B model achieves results comparable to leading models such as GPT-4o and Claude3.5-Sonnet across various multimodal benchmarks, outperforming other generalist models. Code is available at https://github.com/QwenLM/Qwen2-VL .
From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
School of Computer Science, Sichuan University（四川大学计算机学院）；School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School（北京大学深圳研究生院电子与计算机工程学院）；Institute of Artificial Intelligence (TeleAI), China Telecom and Northwestern Polytechnical University（中国电信与西北工业大学人工智能研究院（TeleAI））
Position: Reasoning After Perception Means Reasoning Without Vision
Tsinghua University（清华大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Zhongguancun Academy（中关村学院）；Nanjing University（南京大学）；Nankai University（南开大学）
VTPerception-R1: Enhancing Multimodal Reasoning via Explicit Visual and Textual Perceptual Grounding
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；Shenzhen University（深圳大学）；University of Science and Technology of China（中国科学技术大学）
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion
ZIP Lab, Zhejiang University（浙江大学 ZIP 实验室）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（大数据与智能机器人教育部重点实验室）
GUI-ARP: Enhancing Grounding with Adaptive Region Perception for GUI Agents
Wuhan University（武汉大学）；Sun Yat-sen University（中山大学）；Alibaba Group（阿里巴巴集团）；East China Normal University（华东师范大学）；University of Electronic Science and Technology of China（电子科技大学）
TopoPerception: A Shortcut-Free Evaluation of Global Visual Perception in Large Vision-Language Models
Center for Brain-Inspired Computing Research (CBICR)（脑启发计算研究中心）；Department of Precision Instruments（精密仪器系）；IDG/McGovern Institute for Brain Research（IDG/麦戈文脑研究学院）
AppleVLM: End-to-end Autonomous Driving with Advanced Perception and Planning-Enhanced Vision-Language Models
Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments（深圳先进运动控制系统与现代自动化装备重点实验室）；Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics（广东省智能变形机制与适应机器人省重点实验室）；School of Intelligence Science and Engineering（智能科学与工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；National Key Laboratory of Smart Farm Technologies and Systems（国家智能农业技术与系统重点实验室）；Autonomous Driving Center（自动驾驶中心）；Shanghai Utopilot Technology Co.Ltd.（上海驭目科技有限公司）
Social Perception of Faces in a Vision-Language Model
California Institute of Technology（加州理工学院）；ETH Zurich（苏黎世联邦理工学院）；Computational Social Science（计算社会科学）；Swiss Data Science Center（瑞士数据科学中心）；Empa（瑞士联邦材料科学与技术研究院）
Spatial Colour Mixing Illusions as a Perception Stress Test for Vision-Language Models
National University of Science and Technology POLITEHNICA Bucharest（波兰布加勒斯特技术大学）；IDSIA, The Dalle Molle Institute for Artificial Intelligence（达摩信息技术研究所）
State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；ByteDance, LarkAI（字节跳动 LarkAI）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）
