  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
https://github.com/valeoai/LOST.
