Scholarlink

<p>In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting pre-annotated categories. In this paper, we put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD), which can detect objects beyond training categories without costly collecting new labeled data. We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario, where objects exhibit weak appearance features and arbitrary orientations. Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects. Additionally, the RemoteCLIP model is adopted as an omniscient teacher, which provides rich knowledge to enhance classification capabilities for novel categories. A dynamic label queue is devised to maintain high-quality pseudo-labels during training. By doing so, the proposed CastDet boosts not only novel object proposals but also classification. Furthermore, we extend our approach from horizontal to oriented OVAD with tailored algorithm designs for bounding box representation and pseudo-label generation. Extensive experiments for both tasks on multiple aerial datasets demonstrate the effectiveness of our approach. The code is available at <a href="https://github.com/lizzy8587/CastDet">https://github.com/lizzy8587/CastDet</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation

  • 期刊论文

  • 发表日期：

    2026年3月6日

查看全文
  • Yan Li,
  • Weiwei Guo,
  • Xue Yang,
  • Ning Liao,
  • Shaofeng Zhang,
  • Yi Yu,
  • Wenxian Yu,
  • Junchi Yan

摘要

In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting pre-annotated categories. In this paper, we put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD), which can detect objects beyond training categories without costly collecting new labeled data. We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario, where objects exhibit weak appearance features and arbitrary orientations. Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects. Additionally, the RemoteCLIP model is adopted as an omniscient teacher, which provides rich knowledge to enhance classification capabilities for novel categories. A dynamic label queue is devised to maintain high-quality pseudo-labels during training. By doing so, the proposed CastDet boosts not only novel object proposals but also classification. Furthermore, we extend our approach from horizontal to oriented OVAD with tailored algorithm designs for bounding box representation and pseudo-label generation. Extensive experiments for both tasks on multiple aerial datasets demonstrate the effectiveness of our approach. The code is available at https://github.com/lizzy8587/CastDet.