Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

In this paper, we focus on unsupervised representation learning for skeleton-based action recognition. Existing approaches usually learn action representations by sequential prediction but they suffer from the inability to fully learn semantic information. To address this limitation, we propose a novel framework named Prototypical Contrast and Reverse Prediction (PCRP), which not only creates reverse sequential prediction to learn low-level information (e.g., body posture at every frame) and high-level pattern (e.g., motion order), but also devises action prototypes to implicitly encode semantic similarity shared among sequences. In general, we regard action prototypes as latent variables and formulate PCRP as an expectation-maximization task. Specifically, PCRP iteratively runs (1) E-step as determining the distribution of prototypes by clustering action encoding from the encoder, and (2) M-step as optimizing the encoder by minimizing the proposed ProtoMAE loss, which helps simultaneously pull the action encoding closer to its assigned prototype and perform reverse prediction task. Extensive experiments on N-UCLA, NTU 60, and NTU 120 dataset present that PCRP outperforms state-of-the-art unsupervised methods and even achieves superior performance over some of supervised methods. Codes are available at https://github.com/Mikexu007/PCRP.Comment: Codes are available at https://github.com/Mikexu007/PCR

A Survey on 3D Skeleton-Based Action Recognition Using Learning Method

3D skeleton-based action recognition, owing to the latent advantages of skeleton, has been an active topic in computer vision. As a consequence, there are lots of impressive works including conventional handcraft feature based and learned feature based have been done over the years. However, previous surveys about action recognition mostly focus on the video or RGB data dominated methods, and the scanty existing reviews related to skeleton data mainly indicate the representation of skeleton data or performance of some classic techniques on a certain dataset. Besides, though deep learning methods has been applied to this field for years, there is no related reserach concern about an introduction or review from the perspective of deep learning architectures. To break those limitations, this survey firstly highlight the necessity of action recognition and the significance of 3D-skeleton data. Then a comprehensive introduction about Recurrent Neural Network(RNN)-based, Convolutional Neural Network(CNN)-based and Graph Convolutional Network(GCN)-based main stream action recognition techniques are illustrated in a data-driven manner. Finally, we give a brief talk about the biggest 3D skeleton dataset NTU-RGB+D and its new edition called NTU-RGB+D 120, accompanied with several existing top rank algorithms within those two datasets. To our best knowledge, this is the first research which give an overall discussion over deep learning-based action recognitin using 3D skeleton data.Comment: 8 pages, 6 figure

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

Action recognition via 3D skeleton data is an emerging important topic in these years. Most existing methods either extract hand-crafted descriptors or learn action representations by supervised learning paradigms that require massive labeled data. In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner. Specifically, we first propose to contrast similarity between augmented instances (query and key) of the input skeleton sequence, which are transformed by multiple novel augmentation strategies, to learn inherent action patterns ("pattern-invariance") of different skeleton transformations. Second, to encourage learning the pattern-invariance with more consistent action representations, we propose a momentum LSTM, which is implemented as the momentum-based moving average of LSTM based query encoder, to encode long-term action dynamics of the key sequence. Third, we introduce a queue to store the encoded keys, which allows our model to flexibly reuse proceeding keys and build a more consistent dictionary to improve contrastive learning. Last, by temporally averaging the hidden states of action learned by the query encoder, a novel representation named Contrastive Action Encoding (CAE) is proposed to represent human's action effectively. Extensive experiments show that our approach typically improves existing hand-crafted methods by 10-50% top-1 accuracy, and it can achieve comparable or even superior performance to numerous supervised learning methods.Comment: Accepted by Information Sciences. Our codes are available at https://github.com/Mikexu007/AS-CA