Caption Anything in Video: Fine-grained Object-centric Captioning via
  Spatiotemporal Multimodal Prompting
Authors: name: Yunlong Tang; name: Jing Bi; name: Chao Huang; name: Susan Liang; name: Daiki Shimada; name: Hang Hua; name: Yunzhong Xiao; name: Yizhi Song; name: Pinxin Liu; name: Mingqian Feng; name: Junjia Guo; name: Zhuo Liu; name: Luchuan Song; name: Ali Vosoughi; name: Jinxi He; name: Liu He; name: Zeliang Zhang; name: Jiebo Luo; name: Chenliang Xu
Published: 2025-04-07 22:35:36

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