Action100M：大规模视频动作数据集

摘要

从视觉观察中推断物理行为是推动机器在物理世界中实现智能的基础能力。要实现这一目标，需要涵盖广泛领域的大规模开放词汇视频动作数据集。我们推出Action100M数据集——该数据集从120万条互联网教学视频（总时长14.6年）构建而成，生成约1亿个具有开放词汇动作标注和丰富描述文本的时序定位片段。Action100M通过全自动流程生成：(i) 使用V-JEPA 2嵌入向量进行分层时序分割；(ii) 生成组织为描述树的多层级帧/片段描述；(iii) 通过推理模型在多轮自优化流程下聚合证据，输出结构化标注（简洁/详细动作、执行者、简洁/详细描述）。基于Action100M训练VL-JEPA模型显示出持续的数据规模增益，并在多样化动作识别基准测试中展现出强大的零样本性能，这使Action100M成为视频理解与世界建模可扩展研究的新基石。

English

Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-vocabulary video action datasets that span broad domains. We introduce Action100M, a large-scale dataset constructed from 1.2M Internet instructional videos (14.6 years of duration), yielding O(100 million) temporally localized segments with open-vocabulary action supervision and rich captions. Action100M is generated by a fully automated pipeline that (i) performs hierarchical temporal segmentation using V-JEPA 2 embeddings, (ii) produces multi-level frame and segment captions organized as a Tree-of-Captions, and (iii) aggregates evidence with a reasoning model (GPT-OSS-120B) under a multi-round Self-Refine procedure to output structured annotations (brief/detailed action, actor, brief/detailed caption). Training VL-JEPA on Action100M demonstrates consistent data-scaling improvements and strong zero-shot performance across diverse action recognition benchmarks, establishing Action100M as a new foundation for scalable research in video understanding and world modeling.

Action100M：大规模视频动作数据集

Action100M: A Large-scale Video Action Dataset

摘要

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