Unsupervised Object Localization in the Era of Self-Supervised ViTs: A
  Survey
Oriane Siméoni Éloi Zablocki Spyros Gidaris Gilles Puy Patrick Pérez
The recent enthusiasm for open-world vision systems show the high interest of
the community to perform perception tasks outside of the closed-vocabulary
benchmark setups which have been so popular until now. Being able to discover
objects in images/videos without knowing in advance what objects populate the
dataset is an exciting prospect. But how to find objects without knowing
anything about them? Recent works show that it is possible to perform
class-agnostic unsupervised object localization by exploiting self-supervised
pre-trained features. We propose here a survey of unsupervised object
localization methods that discover objects in images without requiring any
manual annotation in the era of self-supervised ViTs. We gather links of
discussed methods in the repository
https://github.com/valeoai/Awesome-Unsupervised-Object-Localization.