241 citations · 324 across the 6 of their papers we have counts for
6 papers
Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
Haijun Liu, Yanxia Chai, Xiaoheng Tan +2
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). In general, ReID models are alw…
Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
Xinzheng Zhang, Hang Su, Ce Zhang +3
Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detect…
Parameter Sharing Exploration and Hetero-Center based Triplet Loss for Visible-Thermal Person Re-Identification
Haijun Liu, Xiaoheng Tan, Xichuan Zhou
This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID) task, whose goal is to match person images between the daytime visible modality and the…
A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet
Xinzheng Zhang, Hang Su, Ce Zhang +4
In this research, a novel robust change detection approach is presented for imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep learning. Our main contribu…
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection
Xiaoheng Zhang, Yongming Li, Pin Wang +2
Labeled speech data from patients with Parkinsons disease (PD) are scarce, and the statistical distributions of training and test data differ significantly in the existing datasets…
Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection
Xinzheng Zhang, Guo Liu, Ce Zhang +5
Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a much negative effect on chang…