Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
This addresses the challenge of scaling up fine-grained recognition for domains requiring expert labels, though it is incremental as it builds on existing data augmentation methods.
The paper tackles the problem of limited labeled data for fine-grained recognition by proposing Attribute Mix, a data augmentation strategy that mixes semantic attribute features between images, which significantly improves recognition performance without increasing inference costs.
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.