Audio-Visual MLP for Scoring Sport

Figure skating scoring is a challenging task because it requires judging players' technical moves as well as coordination with the background music. Prior learning-based work 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 a 3-5 minutes lasting video, so an extremely long-range representation learning is necessary; 2) prior methods rarely considered the critical audio-visual relationship in their models. Thus, we introduce a multimodal MLP architecture, named Skating-Mixer. It extends the MLP-Mixer-based framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we also 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 outperforms SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to recent competitions that occurred in Beijing 2022 Winter Olympic Games, proving our method has strong robustness.

Skating-Mixer: Multimodal MLP for Scoring Figure Skating

Figure skating scoring is a challenging task because it requires judging players' technical moves as well as coordination with the background music. Prior learning-based work 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 a 3-5 minutes lasting video, so an extremely long-range representation learning is necessary; 2) prior methods rarely considered the critical audio-visual relationship in their models. Thus, we introduce a multimodal MLP architecture, named Skating-Mixer. It extends the MLP-Mixer-based framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we also 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 outperforms SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to recent competitions that occurred in Beijing 2022 Winter Olympic Games, proving our method has strong robustness.