Weakly supervised object detection (WSOD) is an interesting yet challenging task in the computer vision community. The core is to discover the image regions that contain the complete object instances under the image-level supervision. Existing works usually solve this problem via a proposal selection strategy, which selects the most discriminative box regions from the weakly labeled training images. However, these regions usually only contain the discriminative object parts rather than the complete object instances. To address this problem, this article proposes to learn a searching-agent to gradually mine desirable object regions under a region searching paradigm, where we formulate the searching process as a Markov decision process and learn the searching-agent under a deep reinforcement learning framework. To learn such a searching-agent under the weak supervision, we extract the pseudo-complete object regions and the corresponding local discriminative object parts and introduce the obtained pseudo-target-part training pairs into the reinforcement learning process of the search-agent. This learning strategy has twofold advantages: 1) it can mimic the searching process to reveal complete object regions from a certain discriminative part of the object under the weak supervision and 2) it will not suffer from the learning difficulty arise from the long-action sequence that happens when searching from the entire image range. Comprehensive experiments on benchmark data sets demonstrate that by integrating the learned searching-agent with the existing WSOD method, we can achieve better performance than the other state-of-the-art and baseline methods.

中文翻译：

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从判别到完成：用于弱监督对象检测的增强搜索代理学习。

弱监督对象检测（WSOD）在计算机视觉社区中是一项有趣但具有挑战性的任务。核心是在图像级别的监督下发现包含完整对象实例的图像区域。现有的作品通常通过提议选择策略解决此问题，该提议选择策略从标记较弱的训练图像中选择最具区分性的框区域。但是，这些区域通常仅包含可区分的对象部分，而不是完整的对象实例。为了解决这个问题，本文建议学习一种搜索代理，以在区域搜索范式下逐步挖掘所需的对象区域，在此我们将搜索过程公式化为马尔可夫决策过程，并在深度强化学习框架下学习搜索代理。为了在弱监督下学习这样的搜索代理，我们提取了伪完全对象区域和相应的局部区分对象部分，并将获得的伪目标部分训练对引入搜索代理的强化学习过程中。这种学习策略具有双重优势：1）它可以模仿搜索过程，在弱监督的情况下从对象的某个可辨别部分揭示完整的对象区域； 2）它不会遭受长动作序列所带来的学习困难。从整个图像范围进行搜索时会发生这种情况。在基准数据集上进行的综合实验表明，通过将学习到的搜索代理与现有的WSOD方法进行集成，我们可以比其他最新技术和基准方法获得更好的性能。