Reasoning over sports videos for question answering is an important task with
numerous applications, such as player training and information retrieval.
However, this task has not been explored due to the lack of relevant datasets
and the challenging nature it presents. Most datasets for video question
answering (VideoQA) focus mainly on general and coarse-grained understanding of
daily-life videos, which is not applicable to sports scenarios requiring
professional action understanding and fine-grained motion analysis. In this
paper, we introduce the first dataset, named Sports-QA, specifically designed
for the sports VideoQA task. The Sports-QA dataset includes various types of
questions, such as descriptions, chronologies, causalities, and counterfactual
conditions, covering multiple sports. Furthermore, to address the
characteristics of the sports VideoQA task, we propose a new Auto-Focus
Transformer (AFT) capable of automatically focusing on particular scales of
temporal information for question answering. We conduct extensive experiments
on Sports-QA, including baseline studies and the evaluation of different
methods. The results demonstrate that our AFT achieves state-of-the-art
performance.

本文介绍了第一个专门设计用于体育视频问答任务的数据集 ——Sports-QA 数据集，并提出了一种新的自动聚焦 Transformer（AFT），用于自动关注在问题回答中特定时间尺度的信息，通过对 Sports-QA 数据集的广泛实验研究，结果表明我们的 AFT 达到了最先进的性能。