Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learning framework to leverage both the instance-level prior-knowledge and the image-level prior-knowledge based on a novel collaborative self-paced curriculum learning (C-SPCL) regime. Under the weak supervision, C-SPCL can leverage helpful prior-knowledge throughout the whole learning process and collaborate the instance-level confidence inference with the image-level confidence inference in a robust way. Comprehensive experiments on benchmark datasets demonstrate the superior capacity of the proposed C-SPCL regime and the proposed whole framework as compared with state-of-the-art methods along this research line.

中文翻译：

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在协作式自定进度课程学习框架下利用先验知识进行弱监督目标检测

弱监督对象检测是计算机视觉社区中一个有趣但具有挑战性的研究课题，其目的是学习对象模型以仅在图像级注释的监督下定位和检测相应的感兴趣对象。为了解决这个问题，本文建立了一个新的弱监督学习框架，以利用基于新的协作自定进度课程学习（C-SPCL）机制的实例级先验知识和图像级先验知识。在弱监督下，C-SPCL 可以在整个学习过程中利用有用的先验知识，并以稳健的方式将实例级置信推理与图像级置信推理协作。