URECA: Unique Region Caption Anything
Authors: name: Sangbeom Lim, name: Junwan Kim, name: Heeji Yoon, name: Jaewoo Jung, name: Seungryong Kim

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.