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

Related papers

• Object-Centric Framework for Video Moment Retrieval [15.916994168542345]
  Most existing moment retrieval methods rely on temporal sequences of frame-level features that primarily encode global visual and semantic information.<n>In particular temporal dynamics at the object level have been largely overlooked, limiting existing approaches in scenarios requiring object-level reasoning.<n>Our method first extracts query-relevant objects using a scene graph and then graphs from video frames to represent these objects and their relationships.<n>Based on the scene graphs, we construct object-level feature sequences that encode rich visual and semantic information. These sequences are processed by a video tracklet transformer, which models relational-temporal localization among objects over time.
  arXiv  Detail & Related papers  (2025-12-20T17:44:53Z)
• Harnessing Object Grounding for Time-Sensitive Video Understanding [13.599316633905355]
  We propose to improve the time-sensitive video understanding (TSV) capability of video large language models (Video-LLMs) with grounded objects (GO)<n>GO-Tokenizer is a lightweight add-on module for Video-LLMs leveraging off-the-shelf object detectors to encode compact object information on the fly.
  arXiv  Detail & Related papers  (2025-09-08T04:52:00Z)
• Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction [8.214041057237491]
  We introduce temporally-constrained video reasoning segmentation, a novel task formulation that requires models to implicitly infer when target objects become contextually relevant.<n>We also present TCVideoRS, a temporally-constrained video RS dataset containing 52 samples using the videos from the MVOR dataset.
  arXiv  Detail & Related papers  (2025-07-22T15:59:21Z)
• Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation [51.2732688481343]
  We introduce Longtextbf-RVOS, a large-scale benchmark for long-term referring object segmentation.<n>Long-RVOS contains 2,000+ videos of an average duration exceeding 60 seconds, covering a variety of objects.<n>Unlike previous benchmarks that rely solely on the per-frame spatial evaluation, we introduce two metrics to assess the temporal andtemporal consistency.
  arXiv  Detail & Related papers  (2025-05-19T04:52:31Z)
• Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description [56.69740649781989]
  3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI.<n>We introduce Articulate3D, an expertly curated 3D dataset featuring high-quality manual annotations on 280 indoor scenes.<n>We also present USDNet, a novel unified framework capable of simultaneously predicting part segmentation along with a full specification of motion attributes for articulated objects.
  arXiv  Detail & Related papers  (2024-12-02T11:33:55Z)
• Context-Aware Temporal Embedding of Objects in Video Data [0.8287206589886881]
  In video analysis, understanding the temporal context is crucial for recognizing object interactions, event patterns, and contextual changes over time. The proposed model leverages adjacency and semantic similarities between objects from neighboring video frames to construct context-aware temporal object embeddings. Empirical studies demonstrate that our context-aware temporal embeddings can be used in conjunction with conventional visual embeddings to enhance the effectiveness of downstream applications.
  arXiv  Detail & Related papers  (2024-08-23T01:44:10Z)
• VrdONE: One-stage Video Visual Relation Detection [30.983521962897477]
  Video Visual Relation Detection (VidVRD) focuses on understanding how entities over time and space in videos. Traditional methods for VidVRD, challenged by its complexity, typically split the task into two parts: one for identifying what relation are present and another for determining their temporal boundaries. We propose VrdONE, a streamlined yet efficacious one-stage model for VidVRD.
  arXiv  Detail & Related papers  (2024-08-18T08:38:20Z)
• Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video [10.287675722826028]
  This paper introduces Video Spatio-Temporal Disment Networks (VDST-Net) to disentangle information using semi-decoupled temporal knowledge distillation to predict high-quality class activation maps (CAMs) We demonstrate the efficacy of our framework on a public reference dataset and on a more challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames.
  arXiv  Detail & Related papers  (2024-07-22T16:52:32Z)
• Training-Free Robust Interactive Video Object Segmentation [82.05906654403684]
  We propose a training-free prompt tracking framework for interactive video object segmentation (I-PT) We jointly adopt sparse points and boxes tracking, filtering out unstable points and capturing object-wise information. Our framework has demonstrated robust zero-shot video segmentation results on popular VOS datasets.
  arXiv  Detail & Related papers  (2024-06-08T14:25:57Z)
• Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding [61.57847727651068]
  Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query. Most previous works focus on learning frame-level features of each whole frame in the entire video, and directly match them with the textual information. We propose a novel Motion- and Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates optical-flow-guided motion-aware, detection-based appearance-aware, and 3D-aware object-level features.
  arXiv  Detail & Related papers  (2022-03-06T13:57:09Z)
• Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation [95.74244714914052]
  Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich-temporal information online. PCAN outperforms current video instance tracking and segmentation competition winners on Youtube-VIS and BDD100K datasets.
  arXiv  Detail & Related papers  (2021-06-22T17:57:24Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• OVC-Net: Object-Oriented Video Captioning with Temporal Graph and Detail Enhancement [44.228748086927375]
  We introduce the video-based object-oriented video captioning network (OVC)-Net via temporal graph and detail enhancement. To demonstrate the effectiveness, we conduct experiments on the new dataset and compare it with the state-of-the-art video captioning methods.
  arXiv  Detail & Related papers  (2020-03-08T04:34:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.