"Global- and local-aware feature augmentation with semantic orthogonali" by Boyao Shi, Wenbin Li et al.
As for few-shot image classification, recently, some works revisit the standard transfer learning paradigm, i.e., pre-training and fine-tuning, and have achieved some success. However, we find that this kind of methods heavily relies on a naive image-level data augmentation (e.g., cropping and flipping) at the fine-tuning stage, which will easily suffer from the overfitting problem because of the limited-data regime. To tackle this issue, in this paper, we attempt to perform a novel feature-level semantic augmentation at the fine-tuning stage and propose a Global- and Local-aware Feature Augme...
Source: uow.edu.au