Confidence-Aware Spatial-Temporal Attention Graph Convolutional Network for Skeleton-Based Expert-Novice Level Classification

Tatsuki Seino; Naoki Saito; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama

ICASSP 2024accepted0 citations

Confidence-Aware Spatial-Temporal Attention Graph Convolutional Network for Skeleton-Based Expert-Novice Level Classification

Tatsuki Seino, Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama

Abstract

A skeleton-based expert-novice level classification method via Confidence-aware Spatial-Temporal Attention Graph Convolutional Network (ConfSTA-GCN) is presented in this paper. The main contribution of this paper is the realization of the accurate expert-novice level classification by introducing a confidence-aware attention mechanism to the Graph Convolutional Network (GCN)-based classification approach. The expert-novice level classification approach introducing an attention mechanism has a problem that the emphasized features may not be important for accurate classification, which may lead to poor classification performance. To deal with this problem, ConfSTA-GCN introduces the confidence-aware attention mechanism that controls the influence of attention based on the classification confidence measure. Consequently, ConfSTA-GCN solves the problem of the attention mechanism and enables accurate classification. The effectiveness of ConfSTA-GCN for the expert-novice level classification was verified from the experimental results.

BibTeX

@inproceedings{icassp2024_confidenceawares,
  title = {Confidence-Aware Spatial-Temporal Attention Graph Convolutional Network for Skeleton-Based Expert-Novice Level Classification},
  author = {Tatsuki Seino and Naoki Saito and Takahiro Ogawa and Satoshi Asamizu and Miki Haseyama},
  booktitle = {ICASSP 2024},
  year = {2024}
}