Analyzing Fast, Frequent, and Fine-grained (F3) events presents a significant challenge in video analytics and multi-modal LLMs. Current methods struggle to identify events that satisfy all the F3 criteria with high accuracy due to challenges such as motion blur and subtle visual discrepancies. To advance research in video understanding, we introduce F3Set, a benchmark that consists of video datasets for precise F3 event detection. Datasets in F3Set are characterized by their extensive scale and comprehensive detail, usually encompassing over 1,000 event types with precise timestamps and supporting multi-level granularity. Currently, F3Set contains several sports datasets, and this framework may be extended to other applications as well. We evaluated popular temporal action understanding methods on F3Set, revealing substantial challenges for existing techniques. Additionally, we propose a new method, F3ED, for F3 event detections, achieving superior performance. The dataset, model, and benchmark code are available at https://github.com/F3Set/F3Set.
@article{arxiv.2504.08222,
title = {F$^3$Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos},
author = {Zhaoyu Liu and Kan Jiang and Murong Ma and Zhe Hou and Yun Lin and Jin Song Dong},
journal= {arXiv preprint arXiv:2504.08222},
year = {2025}
}