Abstract:
Recent works such as Mixup and Cutmix have demonstrated the effectiveness of augmenting training data for deep models. These methods generate new data by generally blending random image contents and mixing their labels proportionally. However, this strategy tends to produce unreasonable training samples for fine-grained recognition, leading to limited improvement. This is because mixing random image contents may potentially produce images containing destructed object structures. Further, as the category differences mainly reside in small part regions, mixing labels proportionally to the number of mixed pixels might result in label noisy problem. To augment more reasonable training data, we propose Intra-class Part Swapping (InPS) that produces new data by performing attention-guided content swapping on input pairs from the same class. Compared with previous approaches, InPS avoids introducing noisy labels and ensures a likely holistic structure of objects in generated images. We demonstrate InPS outperforms the most recent augmentation approaches in both fine-grained recognition and weakly object localization. Further, by simply incorporating the mid-level feature learning, our proposed method achieves state-of-the-art performance in the literature while maintaining the simplicity and inference efficiency. Our code is publicly available.
