6 citations · 12 across the 6 of their papers we have counts for
6 papers · 1 filter
PromptMID: Modal Invariant Descriptors Based on Diffusion and Vision Foundation Models for Optical-SAR Image Matching
Han Nie, Bin Luo, Jun Liu +4
The ideal goal of image matching is to achieve stable and efficient performance in unseen domains. However, many existing learning-based optical-SAR image matching methods, despite…
Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images
Yi Xiao, Bin Luo, Jun Liu +2
Change Detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to address…
REMM:Rotation-Equivariant Framework for End-to-End Multimodal Image Matching
Han Nie, Bin Luo, Jun Liu +3
We present REMM, a rotation-equivariant framework for end-to-end multimodal image matching, which fully encodes rotational differences of descriptors in the whole matching pipeline…
Source-free Domain Adaptive Object Detection in Remote Sensing Images
Weixing Liu, Jun Liu, Xin Su +2
Recent studies have used unsupervised domain adaptive object detection (UDAOD) methods to bridge the domain gap in remote sensing (RS) images. However, UDAOD methods typically assu…
Object Detection based on OcSaFPN in Aerial Images with Noise
Chengyuan Li, Jun Liu, Hailong Hong +5
Taking the deep learning-based algorithms into account has become a crucial way to boost object detection performance in aerial images. While various neural network representations…
Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?
Weixing Liu, Jun Liu, Bin Luo
Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we pro…