Papers › F³Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos

F³Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos

11 Apr 2025arXiv:2504.08222archive 2025-07-28

Zhaoyu Liu, Kan Jiang, Murong Ma, Zhe Hou, Yun Lin, Jin Song Dong

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.

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