14 citations · 22 across the 5 of their papers we have counts for
5 papers · 1 filter
Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection
Yan Xing, Qi'ao Xu, Jingcheng Zeng +5
Semi-supervised change detection (SSCD) utilizes partially labeled data and a large amount of unlabeled data to detect changes. However, the transformer-based SSCD network does not…
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network
Rui Huang, Qingyi Zhao, Yan Xing +4
Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresult…
C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF
Rui Huang, Binbin Jiang, Qingyi Zhao +3
In this work, we aim to detect the changes caused by object variations in a scene represented by the neural radiance fields (NeRFs). Given an arbitrary view and two sets of scene i…
Background-Mixed Augmentation for Weakly Supervised Change Detection
Rui Huang, Ruofei Wang, Qing Guo +4
Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in t…
IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection
Qing Guo, Ruofei Wang, Rui Huang +2
Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role in diverse real-world applications. Nevertheless, m…