학술논문
Reinforcing Generated Images via Meta-Learning for One-Shot Fine-Grained Visual Recognition
Document Type
Periodical
Author
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Trans. Pattern Anal. Mach. Intell. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 46(3):1455-1463 Mar, 2024
Subject
Language
ISSN
0162-8828
2160-9292
1939-3539
2160-9292
1939-3539
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.