20 citations · 24 across the 6 of their papers we have counts for
6 papers
Relating CNN-Transformer Fusion Network for Change Detection
Yuhao Gao, Gensheng Pei, Mengmeng Sheng +3
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial feature…
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic Segmentation
Tao Chen, XiRuo Jiang, Gensheng Pei +3
Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of u…
A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images
Wang Zhang, Tingting Li, Yuntian Zhang +3
Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote s…
Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
Gensheng Pei, Yazhou Yao, Jianbo Jiao +3
Conventional video object segmentation (VOS) methods usually necessitate a substantial volume of pixel-level annotated video data for fully supervised learning. In this paper, we p…
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Gensheng Pei, Yazhou Yao, Fumin Shen +3
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often…
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Gensheng Pei, Fumin Shen, Yazhou Yao +3
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS). Most of the previous methods directly extract and fuse the motion…