Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning

Self-supervised learning has proved effective for skeleton-based human action understanding, which is an important yet challenging topic. Previous works mainly rely on contrastive learning or masked motion modeling paradigm to model the skeleton relations. However, the sequence-level and joint-level representation learning cannot be effectively and simultaneously handled by these methods. As a result, the learned representations fail to generalize to different downstream tasks. Moreover, combining these two paradigms in a naive manner leaves the synergy between them untapped and can lead to interference in training. To address these problems, we propose Prompted Contrast with Masked Motion Modeling, PCM$^{\rm 3}$, for versatile 3D action representation learning. Our method integrates the contrastive learning and masked prediction tasks in a mutually beneficial manner, which substantially boosts the generalization capacity for various downstream tasks. Specifically, masked prediction provides novel training views for contrastive learning, which in turn guides the masked prediction training with high-level semantic information. Moreover, we propose a dual-prompted multi-task pretraining strategy, which further improves model representations by reducing the interference caused by learning the two different pretext tasks. Extensive experiments on five downstream tasks under three large-scale datasets are conducted, demonstrating the superior generalization capacity of PCM$^{\rm 3}$ compared to the state-of-the-art works. Our project is publicly available at: https://jhang2020.github.io/Projects/PCM3/PCM3.html .

Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition

The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack an adaptive design for different parts, which has a negative impact on the accuracy of action recognition. To realize the adaptive action modeling of both parts, we propose an Actionlet-Dependent Contrastive Learning method (ActCLR). The actionlet, defined as the discriminative subset of the human skeleton, effectively decomposes motion regions for better action modeling. In detail, by contrasting with the static anchor without motion, we extract the motion region of the skeleton data, which serves as the actionlet, in an unsupervised manner. Then, centering on actionlet, a motion-adaptive data transformation method is built. Different data transformations are applied to actionlet and non-actionlet regions to introduce more diversity while maintaining their own characteristics. Meanwhile, we propose a semantic-aware feature pooling method to build feature representations among motion and static regions in a distinguished manner. Extensive experiments on NTU RGB+D and PKUMMD show that the proposed method achieves remarkable action recognition performance. More visualization and quantitative experiments demonstrate the effectiveness of our method. Our project website is available at https://langlandslin.github.io/projects/ActCLR/

Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons' structures and cause semantic loss, due to their resulting unstable training. In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. Specifically, we first design a gradual growing augmentation policy to generate multiple ordered positive pairs, which guide to achieve the consistency of the learned representation from different views. Then, an asymmetric loss is proposed to enforce the hierarchical consistency via a directional clustering operation in the feature space, pulling the representations from strongly augmented views closer to those from weakly augmented views for better generalizability. Meanwhile, we propose and evaluate three kinds of strong augmentations for 3D skeletons to demonstrate the effectiveness of our method. Extensive experiments show that HiCLR outperforms the state-of-the-art methods notably on three large-scale datasets, i.e., NTU60, NTU120, and PKUMMD.

S$^{5}$Mars: Self-Supervised and Semi-Supervised Learning for Mars Segmentation

Deep learning has become a powerful tool for Mars exploration. Mars terrain segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, existing deep-learning-based terrain segmentation methods face two problems: one is the lack of sufficient detailed and high-confidence annotations, and the other is the over-reliance of models on annotated training data. In this paper, we address these two problems from the perspective of joint data and method design. We first present a new Mars terrain segmentation dataset which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a representation-learning-based framework for Mars terrain segmentation, including a self-supervised learning stage (for pre-training) and a semi-supervised learning stage (for fine-tuning). Specifically, for self-supervised learning, we design a multi-task mechanism based on the masked image modeling (MIM) concept to emphasize the texture information of images. For semi-supervised learning, since our dataset is sparsely annotated, we encourage the model to excavate the information of unlabeled area in each image by generating and utilizing pseudo-labels online. We name our dataset and method Self-Supervised and Semi-Supervised Segmentation for Mars (S$^{5}$Mars). Experimental results show that our method can outperform state-of-the-art approaches and improve terrain segmentation performance by a large margin.