Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition

• URL: http://arxiv.org/abs/2204.10689v1
• Date: Fri, 22 Apr 2022 13:11:05 GMT
• Title: Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
• Authors: Satoshi Tsutsui, Yanwei Fu, David Crandall
• Abstract summary: We propose a meta-learning framework to combine generated images with original images, so that the resulting "hybrid" training images improve one-shot learning. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks.
• Score: 36.02360322125622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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