Publications
(See also the personal webpage of our group members)
Group Highlights
(At the end of this page, you can find the full list of publications.)
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.
Zhaoyu Liu, Kan Jiang, Murong Ma, Zhe Hou, Yun Lin, Jin Song Dong
International Conference on Learning Representations (ICLR), 2025
Nowadays there are a wealth of devices and cameras at sports venues and facilities that collect different forms of data. Mining useful insights from such data are crucial for improving the performance of professional athletes. In this paper, we introduce a new interactive tennis analytics framework that can realistically simulate tennis matches using parameters mined from past match data and help reveal in-depth knowledge about tennis strategies. Our approach uses probabilistic model checking to formally evaluate the effectiveness of various strategies and tactics and recommend the best ones for improving players’ chances of winning. Our framework is easily understandable and actionable by players and coaches at any level. We have performed evaluations on tennis matches over the past decade to show the effectiveness of our strategy analytics framework.
Zhaoyu Liu, Kan Jiang, Zhe Hou, Yun Lin, Jin Song Dong
International Conference on Data Mining (ICDM), 2023
This thesis presents a multi-disciplinary research work of applying formal methods, machine learning, and compute vision to a novel application domain, sports analytics.
Kan Jiang
PhD Thesis, National University of Singapore, 2023
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