Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting

• URL: http://arxiv.org/abs/2504.05541v2
• Date: Wed, 09 Apr 2025 02:30:44 GMT
• Title: Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
• Authors: Yunlong Tang, Jing Bi, Chao Huang, Susan Liang, Daiki Shimada, Hang Hua, Yunzhong Xiao, Yizhi Song, Pinxin Liu, Mingqian Feng, Junjia Guo, Zhuo Liu, Luchuan Song, Ali Vosoughi, Jinxi He, Liu He, Zeliang Zhang, Jiebo Luo, Chenliang Xu,
• Abstract summary: We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning.<n>Cat-V integrates three key components: a Segmenter based on SAMI for precise object segmentation across frames, a Temporal Analyzer powered by TRACE-UniVL, and a Captioner using Intern-2.5.<n>Our framework generates detailed, temporally-aware descriptions of objects' attributes, actions, statuses, interactions, and environmental contexts without requiring additional training data.
• Score: 60.58915701973593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects through time. CAT-V integrates three key components: a Segmenter based on SAMURAI for precise object segmentation across frames, a Temporal Analyzer powered by TRACE-Uni for accurate event boundary detection and temporal analysis, and a Captioner using InternVL-2.5 for generating detailed object-centric descriptions. Through spatiotemporal visual prompts and chain-of-thought reasoning, our framework generates detailed, temporally-aware descriptions of objects' attributes, actions, statuses, interactions, and environmental contexts without requiring additional training data. CAT-V supports flexible user interactions through various visual prompts (points, bounding boxes, and irregular regions) and maintains temporal sensitivity by tracking object states and interactions across different time segments. Our approach addresses limitations of existing video captioning methods, which either produce overly abstract descriptions or lack object-level precision, enabling fine-grained, object-specific descriptions while maintaining temporal coherence and spatial accuracy. The GitHub repository for this project is available at https://github.com/yunlong10/CAT-V

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