Bidirectional temporal and frame-segment attention for sparse action segmentation of figure skating

Abstract

Temporal action segmentation is a task for understanding human activities in long-term videos. Most of the efforts have been focused on dense-frame action, which relies on strong correlations between frames. However, in the figure skating scene, technical actions are sparsely shown in the video. This brings new challenges: a large amount of redundant temporal information leads to weak frame correlation. To end this, we propose a Bidirectional Temporal and Frame-Segment Attention Module (FSAM). Specifically, we propose an additional reverse-temporal input stream to enhance frame correlation, learned by fusing bidirectional temporal features. In addition, the proposed FSAM contains a Multi-stage segment-aware GCN and decoder interaction module, aiming to learn the correlation between segment features across time domains and integrate embeddings between frame and segment representations. To evaluate our approach, we propose the Figure Skating Sparse Action Segmentation (FSSAS) dataset: The dataset comprises 100 samples of the Olympic figure skating final and semi-final competition, with more than 50 different men and women athletes. Extensive experiments show that our method achieves an accuracy of 87.75 and an edit score of 90.18 on the FSSAS dataset.