Qwen2.5-VL Technical Report
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language
series, which demonstrates significant advancements in both foundational
capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap
forward in understanding and interacting with the world through enhanced visual
recognition, precise object localization, robust document parsing, and
long-video comprehension. A standout feature of Qwen2.5-VL is its ability to
localize objects using bounding boxes or points accurately. It provides robust
structured data extraction from invoices, forms, and tables, as well as
detailed analysis of charts, diagrams, and layouts. To handle complex inputs,
Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding,
enabling it to process images of varying sizes and videos of extended durations
(up to hours) with second-level event localization. This allows the model to
natively perceive spatial scales and temporal dynamics without relying on
traditional normalization techniques. By training a native dynamic-resolution
Vision Transformer (ViT) from scratch and incorporating Window Attention, we
reduce computational overhead while maintaining native resolution. As a result,
Qwen2.5-VL excels not only in static image and document understanding but also
as an interactive visual agent capable of reasoning, tool usage, and task
execution in real-world scenarios such as operating computers and mobile
devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases
from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model
matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly
excelling in document and diagram understanding. Additionally, Qwen2.5-VL
maintains robust linguistic performance, preserving the core language
competencies of the Qwen2.5 LLM.
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Shuai Bai
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shuai bai
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Keqin Chen
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2025-02-20T15:54:14.529Z
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Xuejing Liu
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Jialin Wang
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jialinwang
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Wenbin Ge
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Wenbin Ge
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Sibo Song
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Kai Dang
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Peng Wang
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Shijie Wang
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Jun Tang
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Humen Zhong
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Yuanzhi Zhu
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Yuanzhi Zhu
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Mingkun Yang
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Jianqiang Wan
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Wei Ding
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weiding
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Zheren Fu
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Zheren Fu
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Yiheng Xu
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Yiheng Xu
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Jiabo Ye
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Jiabo Ye
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Xi Zhang
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Tianbao Xie
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Tianbao Xie
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Zesen Cheng
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Zesen Cheng
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ClownRat
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Hang Zhang
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Zhibo Yang
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Haiyang Xu
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xuhaiyang
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Junyang Lin
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Junyang Lin
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JustinLin610
Qwen2.5-VL Technical Report
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language
series, which demonstrates significant advancements in both foundational
capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap
forward in understanding and interacting with the world through enhanced visual
recognition, precise object localization, robust document parsing, and
long-video comprehension. A standout feature of Qwen2.5-VL is its ability to
localize objects using bounding boxes or points accurately. It provides robust
structured data extraction from invoices, forms, and tables, as well as
detailed analysis of charts, diagrams, and layouts. To handle complex inputs,
Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding,
enabling it to process images of varying sizes and videos of extended durations
(up to hours) with second-level event localization. This allows the model to
natively perceive spatial scales and temporal dynamics without relying on
traditional normalization techniques. By training a native dynamic-resolution
Vision Transformer (ViT) from scratch and incorporating Window Attention, we
reduce computational overhead while maintaining native resolution. As a result,
Qwen2.5-VL excels not only in static image and document understanding but also
as an interactive visual agent capable of reasoning, tool usage, and task
execution in real-world scenarios such as operating computers and mobile
devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases
from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model
matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly
excelling in document and diagram understanding. Additionally, Qwen2.5-VL
maintains robust linguistic performance, preserving the core language
competencies of the Qwen2.5 LLM.
Qwen2.5-VL, the latest vision-language model, advances visual recognition, document parsing, and video comprehension through dynamic resolution processing, Window Attention, and a native Vision Transformer.
Vision Transformer Window Attention dynamic resolution processing bounding boxes dynamic-resolution ViT long-video comprehension document parsing visual recognition spatial scales temporal dynamics real-world scenarios