9 citations · 37 across the 17 of their papers we have counts for
7 papers · 1 filter
Eliminating cross-camera bias for vehicle re-identification
Jinjia Peng, Guangqi Jiang, Dongyan Chen +3
Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of th…
Graph-based Multi-view Binary Learning for Image Clustering
Guangqi Jiang, Huibing Wang, Jinjia Peng +2
Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering…
Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification
Jinjia Peng, Huibing Wang, Xianping Fu
The widespread popularization of vehicles has facilitated all people's life during the last decades. However, the emergence of a large number of vehicles poses the critical but cha…
Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction
Huibing Wang, Jinjia Peng, Xianping Fu
With the development of information technology, we have witnessed an age of data explosion which produces a large variety of data filled with redundant information. Because dimensi…
Mask-guided Style Transfer Network for Purifying Real Images
Tongtong Zhao, Yuxiao Yan, Jinjia Peng +2
Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However,…
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification
Jinjia Peng, Huibing Wang, Tongtong Zhao +1
Vehicle re-identification (reID) is to identify a target vehicle in different cameras with non-overlapping views. When deploy the well-trained model to a new dataset directly, ther…
Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations
Huibing Wang, Jinjia Peng, Xianping Fu
With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information. Most algorithms cannot take…