Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs
This work addresses the problem of automated figure skating scoring for sports analytics, offering a domain-specific solution with incremental improvements in modeling long-term dependencies and audio-visual relationships.
The paper tackles the challenge of figure skating scoring by developing Skating-Mixer, a novel MLP-based multimodal architecture that learns long-term audio-visual representations, achieving state-of-the-art results on public and newly collected datasets.
Figure skating scoring is challenging because it requires judging the technical moves of the players as well as their coordination with the background music. Most learning-based methods cannot solve it well for two reasons: 1) each move in figure skating changes quickly, hence simply applying traditional frame sampling will lose a lot of valuable information, especially in 3 to 5 minutes long videos; 2) prior methods rarely considered the critical audio-visual relationship in their models. Due to these reasons, we introduce a novel architecture, named Skating-Mixer. It extends the MLP framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we collected a high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8 types of programs with 7 different rating metrics, overtaking other datasets in both quantity and diversity. Experiments show the proposed method achieves SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to the recent competitions in Beijing 2022 Winter Olympic Games, proving our method has strong applicability.