Web-supervised network for fine-grained visual classification

Publication Type:
Conference Proceeding
Proceedings - IEEE International Conference on Multimedia and Expo, 2020, 2020-July, pp. 1-6
Issue Date:
Filename Description Size
09102790.pdfPublished version603.6 kB
Adobe PDF
Full metadata record
© 2020 IEEE. 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.
Please use this identifier to cite or link to this item: