111 citations · 140 across the 8 of their papers we have counts for
8 papers
Mutual Graph Learning for Camouflaged Object Detection
Qiang Zhai, Xin Li, Fan Yang +3
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between suc…
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Ao Luo, Xin Li, Fan Yang +3
In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient obje…
Hybrid Graph Neural Networks for Crowd Counting
Ao Luo, Fan Yang, Xin Li +4
Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale…
Learning Quintuplet Loss for Large-scale Visual Geo-Localization
Qiang Zhai
With the maturity of Artificial Intelligence (AI) technology, Large Scale Visual Geo-Localization (LSVGL) is increasingly important in urban computing, where the task is to accurat…
Link Prediction via Matrix Completion
Ratha Pech, Dong Hao, Liming Pan +2
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in…
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
Nan Zhou, Yangyang Xu, Hong Cheng +2
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of…
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model
Linxiao Yang, Jun Fang, Hong Cheng +1
We consider a dictionary learning problem whose objective is to design a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned d…
An Efficient Two-Stage Sparse Representation Method
Chengyu Peng, Hong Cheng, Manchor Ko
There are a large number of methods for solving under-determined linear inverse problem. Many of them have very high time complexity for large datasets. We propose a new method cal…