论文标题
属性混合：细粒度识别的语义数据增强
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
论文作者
论文摘要
收集细粒度的标签通常需要专家级的领域知识，并且无法扩展。在本文中，我们提出了属性混合物，这是属性级别的数据增强策略，以扩展细粒样品。原理在于该属性特征在细粒子类别之间共享，并且可以在图像之间无缝传输。为了实现这一目标，我们提出了一种自动属性挖掘方法，以发现属于同一超级类别的属性，并且属性混合物是通过从两个图像中混合语义上有意义的属性特征来操作的。属性混合是一种简单但有效的数据增强策略，可以显着提高识别性能而不会增加推理预算。此外，由于可以在同一超级类别的图像之间共享属性，因此我们使用来自通用域中的图像进一步丰富了具有属性级别标签的训练样本。广泛使用的细粒基准的实验证明了我们提出的方法的有效性。
Collecting fine-grained labels usually requires expert-level domain knowledge and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples. The principle lies in that attribute features are shared among fine-grained sub-categories, and can be seamlessly transferred among images. Toward this goal, we propose an automatic attribute mining approach to discover attributes that belong to the same super-category, and Attribute Mix is operated by mixing semantically meaningful attribute features from two images. Attribute Mix is a simple but effective data augmentation strategy that can significantly improve the recognition performance without increasing the inference budgets. Furthermore, since attributes can be shared among images from the same super-category, we further enrich the training samples with attribute level labels using images from the generic domain. Experiments on widely used fine-grained benchmarks demonstrate the effectiveness of our proposed method.
