44 citations · 58 across the 12 of their papers we have counts for
5 papers · 1 filter
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Nanqing Liu, Xun Xu, Yongyi Su +2
Segment Anything Model (SAM) is an advanced foundational model for image segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the domain gap betwe…
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
Yingjie Gao, Yanan Zhang, Ziyue Huang +2
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power…
SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
Yitao Zhao, Turgay Celik, Nanqing Liu +2
In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent ful…
Toward distortion-aware change detection in realistic scenarios
Yitao Zhao, Heng-Chao Li, Nanqing Liu +1
In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However…
CLIP-Guided Source-Free Object Detection in Aerial Images
Nanqing Liu, Xun Xu, Yongyi Su +3
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather…