6 citations · 13 across the 6 of their papers we have counts for
6 papers
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…
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…
Learning Weakly-Supervised Contrastive Representations
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary i…
Integrating Auxiliary Information in Self-supervised Learning
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
This paper presents to integrate the auxiliary information (e.g., additional attributes for data such as the hashtags for Instagram images) in the self-supervised learning process.…
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…