Localizing objects in image collections without supervision can help to avoid
expensive annotation campaigns. We propose a simple approach to this problem,
that leverages the activation features of a vision transformer pre-trained in a
self-supervised manner. Our method, LOST, does not require any external object
proposal nor any exploration of the image collection; it operates on a single
image. Yet, we outperform state-of-the-art object discovery methods by up to 8
CorLoc points on PASCAL VOC 2012. We also show that training a class-agnostic
detector on the discovered objects boosts results by another 7 points.
Moreover, we show promising results on the unsupervised object discovery task.
The code to reproduce our results can be found at
this https URL

本文提出了一种不需要昂贵的注释活动的图像集合中的目标本地化的简单方法（称为 LOST），该方法利用了以自我监督方式预训练的视觉转换器的激活特征，同时在 PASCAL VOC 2012 上的实验表明，该方法优于最先进的目标发现方法最高可达 8 CorLoc 点。此外，我们还展示了在发现对象的基础上训练一个不具有类别属性的检测器可以再次提高 7 个点，此外，我们在无监督对象发现任务上也展示了有希望的结果。