  Recent advances in self-supervised visual representation learning have paved
the way for unsupervised methods tackling tasks such as object discovery and
instance segmentation. However, discovering objects in an image with no
supervision is a very hard task; what are the desired objects, when to separate
them into parts, how many are there, and of what classes? The answers to these
questions depend on the tasks and datasets of evaluation. In this work, we take
a different approach and propose to look for the background instead. This way,
the salient objects emerge as a by-product without any strong assumption on
what an object should be. We propose FOUND, a simple model made of a single
$conv1\times1$ initialized with coarse background masks extracted from
self-supervised patch-based representations. After fast training and refining
these seed masks, the model reaches state-of-the-art results on unsupervised
saliency detection and object discovery benchmarks. Moreover, we show that our
approach yields good results in the unsupervised semantic segmentation
retrieval task. The code to reproduce our results is available at
https://github.com/valeoai/FOUND.
