Joint Adversarial and Collaborative Learning for Self-Supervised Action Recognition

• URL: http://arxiv.org/abs/2307.07791v1
• Date: Sat, 15 Jul 2023 12:37:18 GMT
• Title: Joint Adversarial and Collaborative Learning for Self-Supervised Action Recognition
• Authors: Tianyu Guo, Mengyuan Liu, Hong Liu, Wenhao Li, Jingwen Guo, Tao Wang, Yidi Li
• Abstract summary: We present a joint Adversarial and Collaborative Learning framework, which combines Cross-Model Adversarial Learning (CMAL) and Cross-Stream Collaborative Learning (CSCL) CMAL learns single-stream representation by cross-model adversarial loss to obtain more discriminative features. To aggregate and interact with multi-stream information, CSCL is designed by generating similarity pseudo label of ensemble learning as supervision.
• Score: 25.25370509635083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning task to solve the self-supervised skeleton-based action recognition task. These methods usually use multiple data streams (i.e., joint, motion, and bone) for ensemble learning, meanwhile, how to construct a discriminative feature space within a single stream and effectively aggregate the information from multiple streams remains an open problem. To this end, we first apply a new contrastive learning method called BYOL to learn from skeleton data and formulate SkeletonBYOL as a simple yet effective baseline for self-supervised skeleton-based action recognition. Inspired by SkeletonBYOL, we further present a joint Adversarial and Collaborative Learning (ACL) framework, which combines Cross-Model Adversarial Learning (CMAL) and Cross-Stream Collaborative Learning (CSCL). Specifically, CMAL learns single-stream representation by cross-model adversarial loss to obtain more discriminative features. To aggregate and interact with multi-stream information, CSCL is designed by generating similarity pseudo label of ensemble learning as supervision and guiding feature generation for individual streams. Exhaustive experiments on three datasets verify the complementary properties between CMAL and CSCL and also verify that our method can perform favorably against state-of-the-art methods using various evaluation protocols. Our code and models are publicly available at \url{https://github.com/Levigty/ACL}.

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