317 citations · 421 across the 6 of their papers we have counts for
6 papers
Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images
Zhenghui Zhao, Chen Wu, Lixiang Ru +3
Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense d…
Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection
Zhenghui Zhao, Lixiang Ru, Chen Wu
Weakly-supervised change detection (WSCD) aims to detect pixel-level changes with only image-level annotations. Owing to its label efficiency, WSCD is drawing increasing attention…
Token Contrast for Weakly-Supervised Semantic Segmentation
Lixiang Ru, Heliang Zheng, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure…
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
Lixiang Ru, Bo Du, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level labels generally train a classification network to generate the Class Activation Maps (CAMs) as the initial…
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
Lixiang Ru, Bo Du, Chen Wu
Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the land-use trans…
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
Bo Du, Lixiang Ru, Chen Wu +1
Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detectio…