Large-Scale Unsupervised Object DiscoveryDownload PDF

21 May 2021, 20:41 (modified: 26 Oct 2021, 14:34)NeurIPS 2021 PosterReaders: Everyone
Keywords: large-scale, unsupervised, object discovery, ranking, distributed, parallel computation, graph centrality
TL;DR: We propose a novel ranking-based large-scale unsupervised object discovery algorithm.
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~\cite{Lin2014cocodataset} and OpenImages~\cite{openimages} 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 medium-scale 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.
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Code: https://github.com/huyvvo/LOD
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