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 .Comment: Accepted by ACM Multimedia 202

Hypergraph Transformer for Skeleton-based Action Recognition

Skeleton-based action recognition aims to predict human actions given humanjoint coordinates with skeletal interconnections. To model such off-grid datapoints and their co-occurrences, Transformer-based formulations would be anatural choice. However, Transformers still lag behind state-of-the-art methodsusing graph convolutional networks (GCNs). Transformers assume that the inputis permutation-invariant and homogeneous (partially alleviated by positionalencoding), which ignores an important characteristic of skeleton data, i.e.,bone connectivity. Furthermore, each type of body joint has a clear physicalmeaning in human motion, i.e., motion retains an intrinsic relationshipregardless of the joint coordinates, which is not explored in Transformers. Infact, certain re-occurring groups of body joints are often involved in specificactions, such as the subconscious hand movement for keeping balance. Vanillaattention is incapable of describing such underlying relations that arepersistent and beyond pair-wise. In this work, we aim to exploit these uniqueaspects of skeleton data to close the performance gap between Transformers andGCNs. Specifically, we propose a new self-attention (SA) extension, namedHypergraph Self-Attention (HyperSA), to incorporate inherently higher-orderrelations into the model. The K-hop relative positional embeddings are alsoemployed to take bone connectivity into account. We name the resulting modelHyperformer, and it achieves comparable or better performance w.r.t. accuracyand efficiency than state-of-the-art GCN architectures on NTU RGB+D, NTU RGB+D120, and Northwestern-UCLA datasets. On the largest NTU RGB+D 120 dataset, thesignificantly improved performance reached by our Hyperformer demonstrates theunderestimated potential of Transformer models in this field.<br

Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition

Recognizing interactive action plays an important role in human-robot interaction and collaboration. Previous methods use late fusion and co-attention mechanism to capture interactive relations, which have limited learning capability or inefficiency to adapt to more interacting entities. With assumption that priors of each entity are already known, they also lack evaluations on a more general setting addressing the diversity of subjects. To address these problems, we propose an Interactive Spatiotemporal Token Attention Network (ISTA-Net), which simultaneously model spatial, temporal, and interactive relations. Specifically, our network contains a tokenizer to partition Interactive Spatiotemporal Tokens (ISTs), which is a unified way to represent motions of multiple diverse entities. By extending the entity dimension, ISTs provide better interactive representations. To jointly learn along three dimensions in ISTs, multi-head self-attention blocks integrated with 3D convolutions are designed to capture inter-token correlations. When modeling correlations, a strict entity ordering is usually irrelevant for recognizing interactive actions. To this end, Entity Rearrangement is proposed to eliminate the orderliness in ISTs for interchangeable entities. Extensive experiments on four datasets verify the effectiveness of ISTA-Net by outperforming state-of-the-art methods. Our code is publicly available at https://github.com/Necolizer/ISTA-NetComment: IROS 2023 Camera-ready version. Project website: https://necolizer.github.io/ISTA-Net