Related papers: Grafit: Learning fine-grained image representations with coarse labels

Grafit: Learning fine-grained image representations with coarse labels

URL: http://arxiv.org/abs/2011.12982v1
Date: Wed, 25 Nov 2020 19:06:26 GMT
Title: Grafit: Learning fine-grained image representations with coarse labels
Authors: Hugo Touvron, Alexandre Sablayrolles, Matthijs Douze, Matthieu Cord, Herv\'e J\'egou
Abstract summary: This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
Score: 114.17782143848315
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection annotated with coarse labels only. Our network is learned with a nearest-neighbor classifier objective, and an instance loss inspired by self-supervised learning. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods. Our strategy outperforms all competing methods for retrieving or classifying images at a finer granularity than that available at train time. It also improves the accuracy for transfer learning tasks to fine-grained datasets, thereby establishing the new state of the art on five public benchmarks, like iNaturalist-2018.

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