# Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

arXiv: https://arxiv.org/abs/1911.07164 · HTML: https://mlsanity.com/paper/1911.07164/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [Repurposing GANs for One-shot Semantic Part Segmentation](https://mlsanity.com/paper/2103.04379/) (2021)
- [Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions](https://mlsanity.com/paper/2202.11423/) (2022)
- [Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2108.13098/) (2021)
- [Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection](https://mlsanity.com/paper/2212.09317/) (2022)
- [Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition](https://mlsanity.com/paper/2204.10689/) (2022)
- [Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions](https://mlsanity.com/paper/2309.08097/) (2023)
- [DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta](https://mlsanity.com/paper/2207.10271/) (2022)
- [Local Foreground Selection aware Attentive Feature Reconstruction for few-shot fine-grained plant species classification](https://mlsanity.com/paper/2501.06909/) (2025)

## Abstract (arXiv)

One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, an off-the-shelf image generator can be applied to synthesize additional training images, but these synthesized images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. This paper proposes a meta-learning framework to combine generated images with original images, so that the resulting ``hybrid'' training images can 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. The model is trained in an end-to-end manner, and our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks.