论文标题
学习语义增强的功能，以进行细粒度分类
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
论文作者
论文摘要
我们旨在在这封信中提供一种计算廉价但有效的图像分类方法（FGIC）。与以前依赖复杂零件定位模块的方法不同，我们的方法通过增强全球功能的子功能的语义来学习细粒度的特征。具体而言，我们首先通过通过通道置换将CNN的特征通道安排为不同的组来实现亚功能语义。同时，为了增强子功能的可区分性，指导组以通过加权组合正规化强大的可区分性零件激活。我们的方法是参数简约的，可以轻松地集成到骨干模型中，作为仅使用图像级监督的端到端培训的插件模块。实验验证了我们方法的有效性，并验证了其与最先进方法的可比性能。代码可从https://github.com/cswluo/sef获得
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
