We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEF
@article{arxiv.2006.13457,
title = {Learning Semantically Enhanced Feature for Fine-Grained Image Classification},
author = {Wei Luo and Hengmin Zhang and Jun Li and Xiu-Shen Wei},
journal= {arXiv preprint arXiv:2006.13457},
year = {2023}
}
Comments
Accepted by IEEE Signal Processing Letters. 5 pages, 4 figures, 4 tables