VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding

JPShi, Junke Wang, Zuyao You, Bo He, Zuxuan Wu

VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding: 5 upvotes on Hugging Face Daily Papers, #20 of 24 papers on 2026-01-14. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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