title: Qwen2.5-VL Technical Report
summary: 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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paper_title: Qwen2.5-VL Technical Report
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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