一个轻量级通用模型，能跨数据集精准定位视频时间点。
UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding
- 用统一查询格式减少语言差异，避免模型训练时的负向迁移。
- 单个模型在6个数据集上达到顶尖性能，支持长视频定位。
- 体积小于主流大模型100倍，仍保持高精度，适合实际部署。
视频时间定位（VTG）通常依赖于特定数据集的模型，跨领域和查询风格迁移能力差。近期方法尝试用大型多模态语言模型（MLLMs）解决此问题，但计算开销大且视频上下文有限，难以处理长视频。本文提出UniversalVTG，一个通过大规模跨数据集预训练的单一轻量级VTG模型。采用离线查询统一器将不同查询格式映射到统一声明空间，降低语言不匹配，防止简单联合训练带来的负向迁移。结合高效定位头，UniversalVTG可扩展至长视频。在多个基准测试（GoalStep-StepGrounding、Ego4D-NLQ、TACoS、Charades-STA、ActivityNet-Captions）中，单一模型检查点性能优于专用模型。尽管比近期基于MLLM的方法小超过100倍，其准确率仍持平或超越，提供了一种实用的轻量替代方案。
原文摘要 · Abstract (English)
Video temporal grounding (VTG) is typically tackled with dataset-specific models that transfer poorly across domains and query styles. Recent efforts to overcome this limitation have adapted large multimodal language models (MLLMs) to VTG, but their high compute cost and limited video context still hinder long-video grounding. We instead scale unified supervision while keeping the model lightweight. We present UniversalVTG, a single VTG model trained with large-scale cross-dataset pretraining. An offline Query Unifier canonicalizes heterogeneous query formats into a shared declarative space, reducing linguistic mismatch and preventing the negative transfer observed under naïve joint training. Combined with an efficient grounding head, UniversalVTG scales to long, untrimmed videos. Across diverse benchmarks-GoalStep-StepGrounding, Ego4D-NLQ, TACoS, Charades-STA, and ActivityNet-Captions-one UniversalVTG checkpoint achieves state-of-the-art performance versus dedicated VTG models. Moreover, despite being $>100\times$ smaller than recent MLLM-based approaches, UniversalVTG matches or exceeds their accuracy on multiple benchmarks, offering a practical alternative to parameter-heavy MLLMs.
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