Graph convolutional networks (GCNs) have emerged as dom-inant methods for skeleton-based action recognition. How-ever, they still suffer from two problems, namely, neighbor-hood constraints and entangled spatiotemporal feature repre-sentations. Most studies have focused on improving the de-sign of graph topology to solve the first problem but they have yet to fully explore the latter. In this work, we design a dis-entangled spatiotemporal transformer (DSTT) block to over-come the above limitations of GCNs in three steps: (i) feature disentanglement for spatiotemporal decomposition; (ii) global spatiotemporal attention for capturing correlations in the global context; and (iii) local information enhancement for utilizing more local information. Thereon, we propose a novel architecture, named Hierarchical Graph Convolutional skeleton Transformer (HGCT), to employ the complementary advantages of GCN (i.e., local topology, temporal dynamics and hierarchy) and Transformer (i.e., global context and dy-namic attention). HGCT is lightweight and computationally efficient. Quantitative analysis demonstrates the superiority and good interpretability of HGCT. 

/pdf/hierarchical-graph-convolutional-skeleton-transformer-for-1pexcvep.pdf

Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition

Currently, online social network has derived a series of hot research problems, such as link prediction. Many results in undirected and dynamic network have been achieved. Targeted at on-line microblogs, this paper first build user’s dynamic emotional indices and network topological structure features based on time series of user’s contents and network topological information. Then, we improve artificial immune system and deploy it to predict the existence and direction of link. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of temporal information in link prediction.

PRICAI 2018: Trends in Artificial Intelligence

Priori separation graph convolution with long-short term temporal modeling for skeleton-based action recognition

Graph convolutional network (GCN) exhibits advantages in handling non-Euclidean data. Previous works using spatio-temporal graph convolution for skeleton action recognition achieve good performance. However, several limitations still exist. First, the uniform modeling of joint motion assumes that the motion tempo of different joints remains constant, which ignores the dynamic changes in the position offset of each joint during the action. In this work, we propose a robust action feature extractor, graph attention convolutional network with motion tempo enhancement (MTEA-GCN), which captures different joint motion tempos with two streams. Second, the dependencies among bone-connected and spatially separated joints cannot be adequately considered from the graph topology based on the human physical connections. For this reason, we propose a multi-neighborhood graph attention convolution module that fully considers the dependencies among each joint and different neighborhood joints while focusing on discriminative joints. This study experiments on two large-scale skeleton datasets, including Kinetics-Skeleton and NTU RGB+D. Our proposed MTEA-GCN shows good performance with comparable computational complexity and fewer parameters.

Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition.

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Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition.