Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Clothing Landmark Detection Using Deep Networks With Prior of Key Point Associations.

This paper considers a problem of landmark point detection in clothes, which is important and valuable for clothing industry. A novel method for landmark localization has been proposed, which is based on a deep end-to-end architecture using prior of key point associations. With the estimated landmark points as input, a deep network has been proposed to predict clothing categories and attributes. A systematic design of the proposed detecting system is implemented by using deep learning techniques and a large-scale clothes dataset containing 145 000 upper-body clothing images with landmark annotations. Experimental results indicate that clothing categories and attributes can be well classified by using the detected landmark points, which are associated with regions of interest in clothes (e.g., the sleeves and the collars) and share robust learning representation property with respect to large variances of human poses, nonfrontal views, or occlusion. A comprehensive performance evaluation over two newly released datasets is carried out in this paper, showing that the proposed system with deep architecture for clothing landmark detection outperforms the state-of-the-art techniques.