300 citations · 1.2k across the 44 of their papers we have counts for
16 papers · 1 filter
\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
Bo Jiang, Zitai Zhou, Xiao Wang +2
Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to…
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification
Bo Jiang, Xixi Wang, Jin Tang
Discriminative feature representation of person image is important for person re-identification (Re-ID) task. Recently, attributes have been demonstrated beneficially in guiding fo…
GmCN: Graph Mask Convolutional Network
Bo Jiang, Beibei Wang, Jin Tang +1
Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks. Existing GCNs usually conduct feature aggregation on a fixed neighbor…
GLMNet: Graph Learning-Matching Networks for Feature Matching
Bo Jiang, Pengfei Sun, Jin Tang +1
Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning…
Context-Aware Graph Attention Networks
Bo Jiang, Leiling Wang, Jin Tang +1
Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature re…
Edge-guided Non-local Fully Convolutional Network for Salient Object Detection
Zhengzheng Tu, Yan Ma, Chenglong Li +2
Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN based me…
Learning Compact Target-Oriented Feature Representations for Visual Tracking
Chenglong Li, Yan Huang, Liang Wang +2
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundan…
Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network
Bo Jiang, Leiling Wang, Jin Tang +1
Graph Convolutional Networks (GCNs) have been widely studied for compact data representation and semi-supervised learning tasks. However, existing GCNs usually use a fixed neighbor…
Dense Feature Aggregation and Pruning for RGBT Tracking
Yabin Zhu, Chenglong Li, Bin Luo +2
How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm…
Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation
Chenglong Li, Wei Xia, Yan Yan +2
Despite much research progress in image semantic segmentation, it remains challenging under adverse environmental conditions caused by imaging limitations of visible spectrum. Whil…
Multi-Adapter RGBT Tracking
Chenglong Li, Andong Lu, Aihua Zheng +2
The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more at…
RGB-T Image Saliency Detection via Collaborative Graph Learning
Zhengzheng Tu, Tian Xia, Chenglong Li +3
Image saliency detection is an active research topic in the community of computer vision and multimedia. Fusing complementary RGB and thermal infrared data has been proven to be ef…
An Improved Reversible Data Hiding in Encrypted Images using Parametric Binary Tree Labeling
Youqing Wu, Youzhi Xiang, Yutang Guo +2
This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlatio…
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
Xiao Wang, Ziliang Chen, Rui Yang +2
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is…
Multiple Graph Adversarial Learning
Bo Jiang, Ziyan Zhang, Jin Tang +1
Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming wi…
Pedestrian Attribute Recognition: A Survey
Xiao Wang, Shaofei Zheng, Rui Yang +4
Recognizing pedestrian attributes is an important task in the computer vision community due to it plays an important role in video surveillance. Many algorithms have been proposed…