-
[pdf]
[supp]
[bibtex]@InProceedings{Chi_2022_CVPR, author = {Chi, Hyung-gun and Ha, Myoung Hoon and Chi, Seunggeun and Lee, Sang Wan and Huang, Qixing and Ramani, Karthik}, title = {InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {20186-20196} }
InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition
Abstract
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention. Although several studies have focused on encoding a skeleton, less attention has been paid to embed this information into the latent representations of human action. InfoGCN proposes a learning framework for action recognition combining a novel learning objective and an encoding method. First, we design an information bottleneck-based learning objective to guide the model to learn informative but compact latent representations. To provide discriminative information for classifying action, we introduce attention-based graph convolution that captures the context-dependent intrinsic topology of human action. In addition, we present a multi-modal representation of the skeleton using the relative position of joints, designed to provide complementary spatial information for joints. InfoGCN surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.0% on NTU RGB+D 60 cross-subject split, 89.8% on NTU RGB+D 120 cross-subject split, and 97.0% on NW-UCLA.
Related Material