license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
Jiapeng Shi, Junke Wang, Zuyao You, Bo He, Zuxuan Wu✉
🔎 Overview
This paper presents VideoLoom, a unified Video Large Language Model (Video LLM) for joint spatial-temporal understanding. To facilitate the development of fine-grained spatial and temporal localization capabilities, we curate LoomData-8.7k, a human-centric video dataset with temporally grounded and spatially localized captions. With this, VideoLoom achieves state-of-the-art or highly competitive performance across a variety of spatial and temporal benchmarks (e.g., 63.1 J&F on ReVOS for referring video object segmentation, and 48.3 R1@0.7 on Charades-STA for temporal grounding). In addition, we introduce LoomBench, a novel benchmark consisting of temporal, spatial, and compositional video-question pairs, enabling a comprehensive evaluation of Video LLMs from diverse aspects. Collectively, these contributions offer a universal and effective suite for joint spatial-temporal video understanding, setting a new standard in multimodal intelligence.
📜 Citation
If you find our work helpful, please consider giving a star ⭐ and citation 📝
@article{shi2026videoloom,
title={VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding},
author={Shi, Jiapeng and Wang, Junke and You, Zuyao and He, Bo and Wu, Zuxuan},
journal={arXiv preprint arXiv:2601.07290},
year={2026}
}
🤝 Acknowledgements
We refer to Sa2VA and TimeChat to build our codebase. Thanks for their wonderful project.