Grafit: Learning Fine-Grained Image Representations With Coarse Labels
Hugo Touvron, Alexandre Sablayrolles, Matthijs Douze, Matthieu Cord, Hervé Jégou
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.
BibTeX
@inproceedings{iccv2021_grafitlearningfi,
title = {Grafit: Learning Fine-Grained Image Representations With Coarse Labels},
author = {Hugo Touvron and Alexandre Sablayrolles and Matthijs Douze and Matthieu Cord and Hervé Jégou},
booktitle = {ICCV 2021},
year = {2021}
}