mlsanityData to 7 Oct 2026

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

arXiv 2601.07290 ↗First posted 12 Jan 2026cs.CVcited by 5

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Abstract (arXiv)

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 [email protected] 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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 62 of its 73 references that are in the map. First posted on arXiv 12 Jan 2026.

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62 of 73 references.

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understandingcited by 2
  2. 2026Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learningcited by 1
  3. 2026ViSTR-Bench: Can MLLMs Reason from Continuous Visual Cues in Dynamic Scenes?cited by 1
  4. 2026CodecCap: High-Fidelity Codec-Inspired Residual Modeling for Dense Video Captioningcited by 0
  5. 2026AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Modelscited by 0