CLIP-Guided Source-Free Object Detection in Aerial Images
CoRR(2024)
摘要
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. Additionally, high-resolution aerial
images often require substantial storage space and may not be readily
accessible to the public. To address these challenges, we propose a novel
Source-Free Object Detection (SFOD) method. Specifically, our approach begins
with a self-training framework, which significantly enhances the performance of
baseline methods. To alleviate the noisy labels in self-training, we utilize
Contrastive Language-Image Pre-training (CLIP) to guide the generation of
pseudo-labels, termed CLIP-guided Aggregation (CGA). By leveraging CLIP's
zero-shot classification capability, we aggregate its scores with the original
predicted bounding boxes, enabling us to obtain refined scores for the
pseudo-labels. To validate the effectiveness of our method, we constructed two
new datasets from different domains based on the DIOR dataset, named DIOR-C and
DIOR-Cloudy. Experimental results demonstrate that our method outperforms other
comparative algorithms. The code is available at
https://github.com/Lans1ng/SFOD-RS.
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