Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
This addresses the problem of complex and professional sports video analysis for applications like player training and information retrieval, but it is incremental as it builds on existing VideoQA methods with a new dataset and model.
The authors tackled the lack of datasets for video question answering in sports by introducing Sports-QA, a large-scale benchmark, and proposed an Auto-Focus Transformer that achieved state-of-the-art performance on this dataset.
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.