VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
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.
69660cecfc8c4ecc02c7fae4
Jiapeng Shi
claimed_verified
2026-01-14T09:52:57.701Z
657d11ee1ede8a7bb79cc47d
/avatars/b357fbd92885a26c34ededda637c6384.svg
JPShi
JPShi
user
JPShi
69660cecfc8c4ecc02c7fae5
Junke Wang
69660cecfc8c4ecc02c7fae6
Zuyao You
69660cecfc8c4ecc02c7fae7
Bo He
69660cecfc8c4ecc02c7fae8
Zuxuan Wu
VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding
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.
VideoLoom is a unified video large language model that achieves state-of-the-art performance in spatial-temporal video understanding through a specialized dataset and benchmark.
Video LLM spatial-temporal understanding LoomData-8.7k temporally grounded captions spatially localized captions ReVOS referring video object segmentation Charades-STA temporal grounding LoomBench