HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations

by   Luca Franco, et al.

Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeleton-based action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts. When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques. Code is available at https://github.com/paolomandica/HYSP.


page 15

page 17


Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition

Rapid progress and superior performance have been achieved for skeleton-...

Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action Recognition

Self-supervised skeleton-based action recognition with contrastive learn...

Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition

In recent years, self-supervised representation learning for skeleton-ba...

Hyperbolic Contrastive Learning

Learning good image representations that are beneficial to downstream ta...

Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance

This paper presents a novel end-to-end method for the problem of skeleto...

Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments

To integrate action recognition methods into autonomous robotic systems,...

Please sign up or login with your details

Forgot password? Click here to reset