CLIP-Guided Source-Free Object Detection in Aerial Images
Nanqing Liu, Xun Xu, Yongyi Su, Chengxin Liu, Peiliang Gong, Heng-Chao, Li

TL;DR
This paper introduces a source-free object detection method for aerial images that uses CLIP-guided pseudo-label refinement within a self-training framework, improving detection accuracy across diverse domains.
Contribution
It proposes a novel CLIP-guided aggregation technique to enhance pseudo-label quality in source-free domain adaptation for aerial object detection.
Findings
Outperforms existing methods on new DIOR-based datasets
Effective pseudo-label refinement using CLIP guidance
Significant performance improvements demonstrated in experiments
Abstract
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…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques
