T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos
This work solves the problem of accurately detecting events in sports videos for applications like analysis and broadcasting, representing an incremental improvement with a novel method for known bottlenecks.
The paper tackles the problem of precise event spotting in sports videos by introducing T-DEED, which addresses challenges like discriminability among frames and high temporal resolution, achieving state-of-the-art performance on FigureSkating and FineDiving datasets.
In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos. T-DEED addresses multiple challenges in the task, including the need for discriminability among frame representations, high output temporal resolution to maintain prediction precision, and the necessity to capture information at different temporal scales to handle events with varying dynamics. It tackles these challenges through its specifically designed architecture, featuring an encoder-decoder for leveraging multiple temporal scales and achieving high output temporal resolution, along with temporal modules designed to increase token discriminability. Leveraging these characteristics, T-DEED achieves SOTA performance on the FigureSkating and FineDiving datasets. Code is available at https://github.com/arturxe2/T-DEED.