CLIP引导的航拍图像无源目标检测
CLIP-Guided Source-Free Object Detection in Aerial Images
AI总结：
针对航拍图像域适应与数据获取难题，提出基于CLIP引导的无源目标检测方法，利用CLIP零样本分类聚合伪标签得分以缓解噪声，并在新构建的数据集上取得优于对比算法的效果。
摘要：
域适应在航拍图像中至关重要，因为这些图像的视觉表现会因地理位置、时间和天气条件等因素而产生显著差异。此外，高分辨率航拍图像通常需要大量存储空间，且可能无法轻易被公众获取。为了应对这些挑战，我们提出了一种新颖的无源目标检测（SFOD）方法。具体而言，我们的方法始于一个自训练框架，该框架显著提升了基线方法的性能。为了缓解自训练中的噪声标签问题，我们利用对比语言-图像预训练（CLIP）来指导伪标签的生成，称为CLIP引导的聚合（CGA）。通过利用CLIP的零样本分类能力，我们将其得分与原始预测边界框进行聚合，从而获得伪标签的精炼得分。为了验证我们方法的有效性，我们基于DIOR数据集从不同领域构建了两个新数据集，命名为DIOR-C和DIOR-Cloudy。实验结果表明，我们的方法优于其他对比算法。代码可在https://github.com/Lans1ng/SFOD-RS获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
