title: OmniCaptioner: One Captioner to Rule Them All
summary: We propose OmniCaptioner, a versatile visual captioning framework for
  generating fine-grained textual descriptions across a wide variety of visual
  domains. Unlike prior methods limited to specific image types (e.g., natural
  images or geometric visuals), our framework provides a unified solution for
  captioning natural images, visual text (e.g., posters, UIs, textbooks), and
  structured visuals (e.g., documents, tables, charts). By converting low-level
  pixel information into semantically rich textual representations, our framework
  bridges the gap between visual and textual modalities. Our results highlight
  three key advantages: (i) Enhanced Visual Reasoning with LLMs, where
  long-context captions of visual modalities empower LLMs, particularly the
  DeepSeek-R1 series, to reason effectively in multimodal scenarios; (ii)
  Improved Image Generation, where detailed captions improve tasks like
  text-to-image generation and image transformation; and (iii) Efficient
  Supervised Fine-Tuning (SFT), which enables faster convergence with less data.
  We believe the versatility and adaptability of OmniCaptioner can offer a new
  perspective for bridging the gap between language and visual modalities.
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paper_title: OmniCaptioner: One Captioner to Rule Them All
paper_summary: We propose OmniCaptioner, a versatile visual captioning framework for
  generating fine-grained textual descriptions across a wide variety of visual
  domains. Unlike prior methods limited to specific image types (e.g., natural
  images or geometric visuals), our framework provides a unified solution for
  captioning natural images, visual text (e.g., posters, UIs, textbooks), and
  structured visuals (e.g., documents, tables, charts). By converting low-level
  pixel information into semantically rich textual representations, our framework
  bridges the gap between visual and textual modalities. Our results highlight
  three key advantages: (i) Enhanced Visual Reasoning with LLMs, where
  long-context captions of visual modalities empower LLMs, particularly the
  DeepSeek-R1 series, to reason effectively in multimodal scenarios; (ii)
  Improved Image Generation, where detailed captions improve tasks like
  text-to-image generation and image transformation; and (iii) Efficient
  Supervised Fine-Tuning (SFT), which enables faster convergence with less data.
  We believe the versatility and adaptability of OmniCaptioner can offer a new
  perspective for bridging the gap between language and visual modalities.
paper_ai_summary: OmniCaptioner generates detailed captions across various visual domains, enhancing visual reasoning with LLMs, improving image generation tasks, and enabling efficient supervised fine-tuning.
paper_ai_keywords: visual captioning framework low-level pixel information semantically rich textual representations LLMs DeepSeek-R1 long-context captions multimodal scenarios text-to-image generation image transformation supervised fine-tuning