Action100M: A Large-scale Video Action Dataset
This provides a new foundation dataset for scalable research in video understanding and world modeling, addressing a bottleneck for researchers in computer vision and AI.
The authors tackled the problem of limited large-scale video action datasets by introducing Action100M, a dataset of approximately 100 million temporally localized segments from 1.2 million instructional videos, which when used to train VL-JEPA showed consistent data-scaling improvements and strong zero-shot performance across diverse action recognition benchmarks.
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.