One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we propose a meta-learning framework to combine generated images with original images, so that the resulting "hybrid" training images improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.
@article{arxiv.2204.10689,
title = {Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition},
author = {Satoshi Tsutsui and Yanwei Fu and David Crandall},
journal= {arXiv preprint arXiv:2204.10689},
year = {2022}
}
Comments
Accepted to PAMI 2022. arXiv admin note: substantial text overlap with arXiv:1911.07164