Forecasting 3-dimensional skeleton-based human poses from the historical sequence is a classic task, which shows enormous potential in robotics, computer vision, and graphics. Currently, the state-of-the-art methods resort to graph convolutional networks (GCNs) to access the relationships of human joint pairs to formulate this problem. However, human action involves complex interactions among multiple joints, which presents a higher-order correlation overstepping the pairwise (2-order) connection of GCNs. Moreover, joints are typically activated by the parent joint, rather than driving their parent joints, whereas in existing methods, this specific direction of information transmission is ignored. In this work, we propose a novel hybrid directed hypergraph convolution network (H-DHGCN) to model the high-order relationships of the human skeleton with directionality. Specifically, our H-DHGCN mainly involves 2 core components. One is the static directed hypergraph, which is pre-defined according to the human body structure, to effectively leverage the natural relations of human joints. The second is dynamic directed hypergraph (D-DHG). D-DHG is learnable and can be constructed adaptively, to learn the unique characteristics of the motion sequence. In contrast to the typical GCNs, our method brings a richer and more refined topological representation of skeleton data. On several large-scale benchmarks, experimental results show that the proposed model consistently surpasses the latest techniques.

In interactive image segmentation methods, users can participate in and influence the segmentation process through their interactions, such as scribbles or bounding boxes. Similarly, the process of deep interactive segmentation utilizes users’ interactions to guide the network to learn the target of interest. This article mainly considers mouse clicking, which is the simplest interaction mode. Then, how to effectively characterize the click interaction (we call this “click encoding”) and fuse the click-related information with the network are the key issues in a deep interactive segmentation framework. However, the current click encoding method concentrates only on the spatial information of the clicks, so the region affected by each click is difficult to control, and the stability of the network is therefore reduced. Therefore, we propose a feature-interactive map that builds a close relationship between interaction information and target semantics. The affected region of the feature interactive map is determined by semantic information. Furthermore, we introduce an interactive nonlocal block by embedding a feature-interactive map into a nonlocal block, so that the long-range dependencies of the interaction information can be captured. Finally, based on the early fusion strategy, the features of the interactive nonlocal block are fused with the high-level features, thus amplifying the impacts of position and semantics on the final prediction results. Comprehensive experiments demonstrate that our click embedding approach significantly boosts the efficiency of the network and achieves state-of-the-art performance.

One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required.