Localizing Objects with Self-Supervised Transformers and no Labels

Oriane Siméoni    Gilles Puy    Huy V. Vo    Simon Roburin    Spyros Gidaris    Andrei Bursuc    Patrick Pérez    Renaud Marlet    Jean Ponce

BMVC 2021

Abstract

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.

BibTeX

@inproceedings{LOST,
   title = {Localizing Objects with Self-Supervised Transformers and no Labels},
   author = {Oriane Sim\'eoni and Gilles Puy and Huy V. Vo and Simon Roburin and Spyros Gidaris and Andrei Bursuc and Patrick P\'erez and Renaud Marlet and Jean Ponce},
   journal = {Proceedings of the British Machine Vision Conference (BMVC)},
   month = {November},
   year = {2021}
}