CLIP-guided Source-free Object Detection in Aerial Images

no code implementations • 10 Jan 2024 • Nanqing Liu, Xun Xu, Yongyi Su, Chengxin Liu, Peiliang Gong, Heng-Chao Li

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 conditions.

Domain Adaptation Object +3

Toward distortion-aware change detection in realistic scenarios

no code implementations • 10 Jan 2024 • Yitao Zhao, Heng-Chao Li, Nanqing Liu, Rui Wang

The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning.

Change Detection Decoder

Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images

no code implementations • 9 Oct 2023 • Nanqing Liu, Xun Xu, Yingjie Gao, Heng-Chao Li

Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data.

Object object-detection +2

Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images

no code implementations • 13 Mar 2023 • Nanqing Liu, Xun Xu, Turgay Celik, Zongxin Gan, Heng-Chao Li

Object detection in remote sensing images relies on a large amount of labeled data for training.

Few-Shot Object Detection Meta-Learning +2