T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos

Artur Xarles, Sergio Escalera, Thomas B. Moeslund, Albert Clapés
4/8/2024
cs.CV

Official code: arturxe2/T-DEED (39 stars, GPL-3.0, last commit 2026-01-07).

Abstract

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.

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Code Implementations(1)

arturxe2/T-DEEDOfficial100%
GPL-3.0

PyTorch Implementation of "T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos", 10th International Workshop on Computer Vision in Sports (CVsports) at CVPR 2024.

3915PythonApr 5, 20249 months agoGPL-3.0

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Cite this paper

@article{xarles2024tdeed,
  title  = {T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos},
  author = {Artur Xarles and Sergio Escalera and Thomas B. Moeslund and Albert Clapés},
  year   = {2024},
  eprint = {2404.05392},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.05392v2}
}

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