title: Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at
    Any Resolution
summary: 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.
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paper_authors.fullname: jialinwang
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paper_authors.name: Wenbin Ge
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paper_authors.name: Yang Fan
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paper_title: Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at
    Any Resolution
paper_summary: 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.
paper_ai_summary: The Qwen2-VL Series uses Naive Dynamic Resolution and Multimodal Rotary Position Embedding to enhance visual processing and achieves competitive performance on multimodal benchmarks.
paper_ai_keywords: Naive Dynamic Resolution visual tokens Multimodal Rotary Position Embedding LVLMs Qwen2-VL GPT-4 Claude3.5-Sonnet multimodal benchmarks