Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at
  Any Resolution
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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Peng Wang
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Shuai Bai
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Keqin Chen
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Xuejing Liu
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Jialin Wang
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Wenbin Ge
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Yang Fan
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Mengfei Du
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Rui Men
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Dayiheng Liu
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Chang Zhou
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Jingren Zhou
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Junyang Lin
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Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at
  Any Resolution
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.
The Qwen2-VL Series uses Naive Dynamic Resolution and Multimodal Rotary Position Embedding to enhance visual processing and achieves competitive performance on multimodal benchmarks.
Naive Dynamic Resolution visual tokens Multimodal Rotary Position Embedding LVLMs Qwen2-VL GPT-4 Claude3.5-Sonnet multimodal benchmarks