179 citations · 222 across the 7 of their papers we have counts for
7 papers
Eliminating Gradient Conflict in Reference-based Line-Art Colorization
Zekun Li, Zhengyang Geng, Zhao Kang +2
Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on th…
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
Li Zhou, Wenyu Chen, Dingyi Zeng +4
Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning o…
Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
Zhengrui Ma, Zhao Kang, Guangchun Luo +1
Finding a suitable data representation for a specific task has been shown to be crucial in many applications. The success of subspace clustering depends on the assumption that the…
Multi-graph Fusion for Multi-view Spectral Clustering
Zhao Kang, Guoxin Shi, Shudong Huang +4
A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and…
Multiple Partitions Aligned Clustering
Zhao Kang, Zipeng Guo, Shudong Huang +4
Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering m…
Latent Multi-view Semi-Supervised Classification
Xiaofan Bo, Zhao Kang, Zhitong Zhao +2
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most…
Low-rank Kernel Learning for Graph-based Clustering
Zhao Kang, Liangjian Wen, Wenyu Chen +1
Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. Howev…