Grafit: Learning Fine-Grained Image Representations with Coarse Labels
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.
Cite
Text
Touvron et al. "Grafit: Learning Fine-Grained Image Representations with Coarse Labels." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.00091Markdown
[Touvron et al. "Grafit: Learning Fine-Grained Image Representations with Coarse Labels." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/touvron2021iccv-grafit/) doi:10.1109/ICCV48922.2021.00091BibTeX
@inproceedings{touvron2021iccv-grafit,
title = {{Grafit: Learning Fine-Grained Image Representations with Coarse Labels}},
author = {Touvron, Hugo and Sablayrolles, Alexandre and Douze, Matthijs and Cord, Matthieu and Jégou, Hervé},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {874-884},
doi = {10.1109/ICCV48922.2021.00091},
url = {https://mlanthology.org/iccv/2021/touvron2021iccv-grafit/}
}