@inproceedings{b66570e2f80d4e1f955ff50afa6c28e8,
title = "Web-supervised network for fine-grained visual classification",
abstract = "Fine-grained visual classification (FGVC) is a tough task due to its high annotation cost of the fine-grained subcategories. To build a large-scale dataset at low manual cost, straightforwardly learning from web images for FGVC has attracted broad attention. However, there exist two characteristics in the need of concerning for the web dataset: 1) Noisy images; 2) A large proportion of hard examples. In this paper, we propose a simple yet effective approach to deal with noisy images and hard examples during training. Our method is a pure web-supervised method for FGVC. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to the state-of-the-art web-supervised methods. The data and source code of this work have been posted available at: https://github.com/NUST-Machine-Intelligence-Laboratory/WSNFG.",
keywords = "Fine-grained, Recognition, Web-supervised",
author = "Chuanyi Zhang and Yazhou Yao and Jiachao Zhang and Jiaxin Chen and Pu Huang and Jian Zhang and Zhenmin Tang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Multimedia and Expo, ICME 2020 ; Conference date: 06-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
doi = "10.1109/ICME46284.2020.9102790",
language = "英语",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2020 IEEE International Conference on Multimedia and Expo, ICME 2020",
address = "美国",
}