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In an effort to minimize the manual annotation cost for the training of object detectors based on deep learning, we reflect on the role of active learning in object detection when combined with other sources of supervision. In doing so, we highlight the need to harmonize the approaches so that they can develop their full potential. Ultimately, the active learning oracle should only provide supervision for samples that cannot be covered by other, cheaper, forms of supervision.

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Active Learning and the Various Flavors of Supervision for Object Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Nils Bischoff,
  • Sven Tomforde

摘要

In an effort to minimize the manual annotation cost for the training of object detectors based on deep learning, we reflect on the role of active learning in object detection when combined with other sources of supervision. In doing so, we highlight the need to harmonize the approaches so that they can develop their full potential. Ultimately, the active learning oracle should only provide supervision for samples that cannot be covered by other, cheaper, forms of supervision.