Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.
翻译:精细的视觉分类旨在识别属于同一类别中多个子类别的图像,这是一项具有挑战性的任务,因为高度分散的类别之间有着内在的微妙差异。大多数现有方法只将个人图像作为输入,这可能会限制模型识别不同图像的对比线索的能力。在本文中,我们提出了一种有效的方法,称为渐进式共同关注网络(PCA-Net)来解决这一问题。具体地说,我们通过鼓励同一类别图像组合中各特征渠道之间的互动来计算频道上的相似性,以捕捉共同的歧视性特征。考虑到补充信息对于识别来说也至关重要,我们删除了通过频道互动而加强的突出领域,以迫使网络聚焦于其他歧视区域。拟议模型在三个精细的视觉分类基准数据集:CUB-200-2011、斯坦福汽车和FGVC飞机上取得了竞争性结果。