CLIP-guided Source-free Object Detection in Aerial Images
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. 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 is built
upon a self-training framework; however, self-training can lead to inaccurate
learning in the absence of labeled training data. To address this issue, we
further integrate Contrastive Language-Image Pre-training (CLIP) to guide the
generation of pseudo-labels, termed CLIP-guided Aggregation. By leveraging
CLIP's zero-shot classification capability, we use it to aggregate 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. Experiments demonstrate that our method outperforms other
comparative algorithms.