Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set. In this work, we study the problem of training an object detector from one or few images with image-level labels and a larger set of completely unlabeled images. This is an extreme case of semi-supervised learning where the labeled data are not enough to bootstrap the learning of a detector. Our solution is to train a weakly-supervised student detector model from image-level pseudo-labels generated on the unlabeled set by a teacher classifier model, bootstrapped by region-level similarities to labeled images. Building upon the recent representative weakly-supervised pipeline PCL [1], our method can use more unlabeled images to achieve performance competitive or superior to many recent weakly-supervised detection solutions. Code will be made available at https://github.com/zhaohui-yang/NSOD.

弱监督对象检测试图通过免除对边界框的需求来限制监督量，但仍然假设整个训练集上的图像级标签。在这项工作中，我们研究了从一个或几个具有图像级标签的图像和一组更大的完全未标记的图像训练目标检测器的问题。这是半监督学习的一种极端情况，其中标记数据不足以引导检测器的学习。我们的解决方案是从教师分类器模型在未标记集上生成的图像级伪标签训练弱监督学生检测器模型，并通过区域级与标记图像的相似性进行引导。基于最近的代表性弱监督管道 PCL [1]，我们的方法可以使用更多未标记的图像来实现与许多最近的弱监督检测解决方案竞争或优于其的性能。代码将在 https://github.com/zhaohui-yang/NSOD 上提供。