Large-Scale Unsupervised Object Discovery

Huy V. Vo   Elena Sizikova   Cordelia Schmid   Patrick Pérez   Jean Ponce

NeurIPS 2021

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Abstract

Existing approaches to unsupervised object discovery (UOD) do not scale up to large datasets without approximations that compromise their performance. We propose a novel formulation of UOD as a ranking problem, amenable to the arsenal of distributed methods available for eigenvalue problems and link analysis. Through the use of self-supervised features, we also demonstrate the first effective fully unsupervised pipeline for UOD. Extensive experiments on COCO [42] and OpenImages [35] show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for mediumscale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1.7M images. In the multi-object discovery setting where multiple objects are sought in each image, the proposed LOD is over 14% better in average precision (AP) than all other methods for datasets ranging from 20K to 1.7M images. Using self-supervised features, we also show that the proposed method obtains state-of-the-art UOD performance on OpenImages.



BibTeX

@inproceedings{Vo21LOD,
  title     = {Large-Scale Unsupervised Object Discovery},
  author    = {Vo, Huy V. and Sizikova, Elena and Schmid, 
               Cordelia and P{\'e}rez, Patrick and Ponce, Jean},
  booktitle = {Advances in Neural Information Processing Systems 34 ({NeurIPS})}
  year      = {2021},
}