Two papers accepted by computer vision conference

Apr 08, 2019

Two papers from the BMI Associate Professor Qiang Cheng's group have been accepted by IEEE CVPR2019, June 16th - 20th 2019, the premier annual computer vision conference.

Exploiting Edge Features for Graph Neural Networks
By Liyu Gong and Qiang Cheng

Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g., graph convolutional networks (GCN) and graph attention networks (GAT), inadequately utilize edge features, especially multidimensional edge features. In this paper, we build a new framework for a family of new graph neural network models that can more sufficiently exploit edge features, including those of undirected or multi-dimensional edges. The proposed framework can consolidate current graph neural network models, e.g., GCN and GAT. The proposed framework and new models have the following novelties: First, we propose to use doubly stochastic normalization of graph edge features instead of the commonly used row or symmetric normalization approaches used in current graph neural networks. Second, we construct new formulas for the operations in each individual layer so that they can handle multi-dimensional edge features. Third, for the proposed new framework, edge features are adaptive across network layers. As a result, our proposed new framework and new models are able to exploit a rich source of graph edge information. We apply our new models to graph node classification on several citation networks, whole graph classification, and regression on several molecular datasets. Compared with the current state-of-the-art methods, i.e., GCNs and GAT, our models obtain better performance, which testify to the importance of exploiting edge features in graph neural networks.

RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
By Chong Peng, Chenglizhao Chen, Zhao Kang, Jianbo Li, and Qiang Cheng

Robust principal component analysis (RPCA) has drawn significant attention due to its powerful capability in recovering low-rank matrices as well as successful applications in various real-world problems. The current state-of-the-art algorithms usually need to solve singular value decomposition of large matrices, which generally has at least a quadratic or even cubic complexity. This drawback has limited the application of RPCA in solving real-world problems. To combat this drawback, in this paper we propose a new type of RPCA method, RES-PCA, which is linearly efficient and scalable in both data size and dimension. For comparison purpose, AltProj, an existing scalable approach to RPCA requires the precise knowledge of the true rank; otherwise, it may fail to recover low-rank matrices. By contrast, our method works with or without knowing the true rank; even when both methods work, our method is faster. Extensive experiments have been performed and testified to the effectiveness of proposed method quantitatively and in visual quality, which suggests that our method is suitable to be employed as a light-weight, scalable component for RPCA in any application pipelines.