title: URECA: Unique Region Caption Anything
summary: Region-level captioning aims to generate natural language descriptions for
  specific image regions while highlighting their distinguishing features.
  However, existing methods struggle to produce unique captions across
  multi-granularity, limiting their real-world applicability. To address the need
  for detailed region-level understanding, we introduce URECA dataset, a
  large-scale dataset tailored for multi-granularity region captioning. Unlike
  prior datasets that focus primarily on salient objects, URECA dataset ensures a
  unique and consistent mapping between regions and captions by incorporating a
  diverse set of objects, parts, and background elements. Central to this is a
  stage-wise data curation pipeline, where each stage incrementally refines
  region selection and caption generation. By leveraging Multimodal Large
  Language Models (MLLMs) at each stage, our pipeline produces distinctive and
  contextually grounded captions with improved accuracy and semantic diversity.
  Building upon this dataset, we present URECA, a novel captioning model designed
  to effectively encode multi-granularity regions. URECA maintains essential
  spatial properties such as position and shape through simple yet impactful
  modifications to existing MLLMs, enabling fine-grained and semantically rich
  region descriptions. Our approach introduces dynamic mask modeling and a
  high-resolution mask encoder to enhance caption uniqueness. Experiments show
  that URECA achieves state-of-the-art performance on URECA dataset and
  generalizes well to existing region-level captioning benchmarks.
paper_authors._id: 67f495437e1624ebbaf2d90e
paper_authors.name: Sangbeom Lim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:51:37.341Z
paper_authors._id: 64842d1edc475c315e41123a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64842d1edc475c315e41123a/Yqam_dy0MnQm4Rn1_1_4N.jpeg
paper_authors.fullname: Sangbeom Lim
paper_authors.type: user
paper_authors.user: SammyLim
paper_authors._id: 67f495437e1624ebbaf2d90f
paper_authors.name: Junwan Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:51:34.302Z
paper_authors._id: 64c2c45ae818eec6128fdda3
paper_authors.avatarUrl: /avatars/d4399e25e6399345e263c7902789047e.svg
paper_authors.fullname: Jun-Wan KIM
paper_authors.type: user
paper_authors.user: junwann
paper_authors._id: 67f495437e1624ebbaf2d910
paper_authors.name: Heeji Yoon
paper_authors._id: 67f495437e1624ebbaf2d911
paper_authors.name: Jaewoo Jung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:51:32.321Z
paper_authors._id: 65d02dc017e2b305e0d7bf4f
paper_authors.avatarUrl: /avatars/2a50fd0541e7b0e200c577a661956696.svg
paper_authors.fullname: Jaewoo Jung
paper_authors.type: user
paper_authors.user: crepejung00
paper_authors._id: 67f495437e1624ebbaf2d912
paper_authors.name: Seungryong Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T09:13:34.541Z
paper_authors._id: 65cf717450818a335a1d3021
paper_authors.avatarUrl: /avatars/382a0e0f40f661cda1b2531e3e6ea2ee.svg
paper_authors.fullname: Seungryong Kim
paper_authors.type: user
paper_authors.user: seungryong
paper_title: URECA: Unique Region Caption Anything
paper_summary: Region-level captioning aims to generate natural language descriptions for
  specific image regions while highlighting their distinguishing features.
  However, existing methods struggle to produce unique captions across
  multi-granularity, limiting their real-world applicability. To address the need
  for detailed region-level understanding, we introduce URECA dataset, a
  large-scale dataset tailored for multi-granularity region captioning. Unlike
  prior datasets that focus primarily on salient objects, URECA dataset ensures a
  unique and consistent mapping between regions and captions by incorporating a
  diverse set of objects, parts, and background elements. Central to this is a
  stage-wise data curation pipeline, where each stage incrementally refines
  region selection and caption generation. By leveraging Multimodal Large
  Language Models (MLLMs) at each stage, our pipeline produces distinctive and
  contextually grounded captions with improved accuracy and semantic diversity.
  Building upon this dataset, we present URECA, a novel captioning model designed
  to effectively encode multi-granularity regions. URECA maintains essential
  spatial properties such as position and shape through simple yet impactful
  modifications to existing MLLMs, enabling fine-grained and semantically rich
  region descriptions. Our approach introduces dynamic mask modeling and a
  high-resolution mask encoder to enhance caption uniqueness. Experiments show
  that URECA achieves state-of-the-art performance on URECA dataset and
  generalizes well to existing region-level captioning benchmarks.
paper_ai_summary: The URECA dataset and model address multi-granularity region captioning by ensuring unique and contextually grounded captions through a stage-wise pipeline and dynamic mask modeling.
paper_ai_keywords: region-level captioning URECA dataset multimodal large language models MLMLs stage-wise data curation pipeline dynamic mask modeling high-resolution mask encoder spatial properties