We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, graph has rich variable-size structures, that suggests complex relationships between labels and subgraphs. Uncovering these relations might hold the keys of classification performance and explainability. To this end, we design GAML (Graph Attentional Multi-Label learning), a graph neural...

As a major family of semi-supervised learning (SSL), graph-based SSL has recently attracted considerable interest in the machine learning community along with application areas such as video semantic analysis. In this paper, we analyze the connections between graph-based SSL and partial differential equation(PDE) based diffusion. From the viewpoint of PDE-based diffusion, the label propagation ...

In the literature, most existing graph-based semisupervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between l...

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over last few years, almost existing models follow same training paradigm: they treat both object predicate classification as single-label problem, ground-truths are one-hot target labels. However, this prevalent paradigm ov...

Multi-label classification aims to recognize multiple objects or attributes from images. The key solving this issue relies on effectively characterizing the inter-label correlations dependencies, which bring prevailing graph neural network. However, current methods often use co-occurrence probability of labels based training set as adjacency matrix model correlation, is greatly limited by datas...

PURPOSE Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images. METHODS A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each sup...