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The fine-grained action analysis of the existing action datasets ischallenged by insufficient action categories, low fine granularities, limitedmodalities, and tasks. In this paper, we propose a Multi-modality andMulti-task dataset of Figure Skating (MMFS) which was collected from the WorldFigure Skating Championships. MMFS, which possesses action recognition andaction quality assessment, captures RGB, skeleton, and is collected the scoreof actions from 11671 clips with 256 categories including spatial and temporallabels. The key contributions of our dataset fall into three aspects asfollows. (1) Independently spatial and temporal categories are first proposedto further explore fine-grained action recognition and quality assessment. (2)MMFS first introduces the skeleton modality for complex fine-grained actionquality assessment. (3) Our multi-modality and multi-task dataset encouragemore action analysis models. To benchmark our dataset, we adopt RGB-based andskeleton-based baseline methods for action recognition and action qualityassessment.

Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating