In this paper, we consider the problem of leveraging existing fully labeled categories to improve the weakly supervised detection (WSD) of new object categories, which we refer to as mixed supervised detection (MSD). Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD. In our framework, we first learn domain-invariant objectness knowledge from the existing fully labeled categories. The knowledge is modeled based on invariant features that are robust to the distribution discrepancy between the existing categories and new categories; therefore the resulting knowledge would generalize well to new categories and could assist detection models to reject distractors (e.g., object parts) in weakly labeled images of new categories. Under the guidance of learned objectness knowledge, we utilize multiple instance learning (MIL) to model the concepts of both objects and distractors and to further improve the ability of rejecting distractors in weakly labeled images. Our robust objectness transfer approach outperforms the existing MSD methods, and achieves state-of-the-art results on the challenging ILSVRC2013 detection dataset and the PASCAL VOC datasets.

中文翻译：

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具有鲁棒性目标转移的混合监督对象检测

在本文中，我们考虑了利用现有的完全标记类别来改善新对象类别的弱监督检测（WSD）的问题，我们将其称为混合监督检测（MSD）。与将直接将预先训练的对象检测器从现有类别直接转移到新类别的以前的MSD方法不同，我们提出了一种更合理，更健壮的MSD对象转移方法。在我们的框架中，我们首先从现有的完全标记类别中学习领域不变的对象知识。知识是基于不变特征建模的，这些不变特征对现有类别和新类别之间的分布差异具有鲁棒性；因此，由此产生的知识将很好地推广到新类别，并可以协助检测模型拒绝干扰因素（例如，对象部分）在新类别中标记较弱的图像中。在学习的客观知识的指导下，我们利用多实例学习（MIL）对物体和干扰物的概念进行建模，并进一步提高在弱标签图像中拒绝干扰物的能力。我们强大的目标转移方法优于现有的MSD方法，并在具有挑战性的ILSVRC2013检测数据集和PASCAL VOC数据集上实现了最新的结果。