In this work, we propose a dual-stream structured graph convolution network ( DS-SGCN ) to solve the skeleton-based action recognition problem. The spatio-temporal coordinates and appearance contexts of the skeletal joints are jointly integrated into the graph convolution learning process on both the video and skeleton modalities. To effectively represent the skeletal graph of discrete joints, we create a structured graph convolution module specifically designed to encode partitioned body parts along with their dynamic interactions in the spatio-temporal sequence. In more detail, we build a set of structured intra-part graphs, each of which can be adopted to represent a distinctive body part (e.g., left arm, right leg, head). The inter-part graph is then constructed to model the dynamic interactions across different body parts; here each node corresponds to an intra-part graph built above, while an edge between two nodes is used to express these internal relationships of human movement. We implement the graph convolution learning on both intra- and inter-part graphs in order to obtain the inherent characteristics and dynamic interactions, respectively, of human action. After integrating the intra- and inter-levels of spatial context/coordinate cues, a convolution filtering process is conducted on time slices to capture these temporal dynamics of human motion. Finally, we fuse two streams of graph convolution responses in order to predict the category information of human action in an end-to-end fashion. Comprehensive experiments on five single/multi-modal benchmark datasets (including NTU RGB+D 60, NTU RGB+D 120, MSR-Daily 3D, N-UCLA, and HDM05) demonstrate that the proposed DS-SGCN framework achieves encouraging performance on the skeleton-based action recognition task.

As an important building block in automatic medical applications, image segmentation has made a great progress due to the data-driving mechanism of deep architecture. Recently, numerous methods have been proposed to boost the segmentation performance based on U-shape network. However, they often built feature encoders with only one data routine, which have limited the representation ability of the networks. Although some methods applied multiple learning paths to fix this problem, the deep supervision techniques are required to monitor the training status at individual path, which may bring extra burden to practical usage of the algorithm. Additionally, under these frameworks, the semantic gap between different paths may interfere with model's learning performance, and the potential transduction ability of skip connections still needs further investigation. To address these issues, we introduce a novel medical image segmentation framework, namely AFT-Net, in which an attention-based data fusion model is proposed to effectively cooperate with multi-stream encoder. By progressively accumulating the features from different paths, our method can establish meaningful connections between structural and semantic features, while keeping an integral and flexible layout without deeply customized supervisions. Extensive experiments on two medical image data sets demonstrate that our method is able to acquire image features with both diversity and quality, thereby outperforms current state-of-the-art segmentation methods.

Micro-expression recognition is a challenging problem due to its short duration and low intensity. Most previous work on micro-expression mainly used the handcrafted features. Recently, deep learning methods were also employed for some difficult face recognition tasks. This paper presents a new framework to recognize micro-expression by combining handcrafted features and deep features. The employed handcrafted feature is called Local Gabor Binary Pattern from Three Orthogonal Panels (LGBP-TOP) feature. LGBP-TOP combines spatial and temporal analysis to encode the local facial movements. The employed deep feature is based on the Convolutional Neural Network (CNN) model trained on the micro-expression dataset. And then, the sparse multi-task learning framework with adaptive penalty term is employed to remove the irrelevant information from the combined LGBP-TOP and CNN features. The experimental evaluation is performed on two widely used micro-expression databases. The results demonstrate that the proposed approach achieves a competitive performance compared with other popular micro-expression recognition methods.

Accurate head pose estimation is significant for many applications such as face recognition and human-computer interaction. In this paper, we treat the head pose estimation as a classification problem and employ the Lie Algebrized Gaussians (LAG) feature as the representation approach for head image. The LAG feature, which is built on Gausssian Mixture Model (GMM), has the capability to preserve the structure of Gaussian components in the original Lie group manifold. Moreover, to keep more spatial structure information of the image, LAG is operated on many subregions of the image. As a result, these properties of LAG enable it to reflect the pose characteristic of the head image well and possess powerful discriminative ability in pose classification. Experiments on CMU Pose, Illumination, and Expression (PIE) and Pointing'04 benchmarks show state-of-the-art performance and demonstrate that LAG represents the head pose characteristic well.