# Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

arXiv: https://arxiv.org/abs/1807.10916 · HTML: https://mlsanity.com/paper/1807.10916/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2018.

## Built on by (most cited first, 25 of 25)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2305.12398/) (2023)
- [TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation](https://mlsanity.com/paper/2106.06326/) (2021)
- [MetAug: Contrastive Learning via Meta Feature Augmentation](https://mlsanity.com/paper/2203.05119/) (2022)
- [Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey](https://mlsanity.com/paper/2303.08557/) (2023)
- [Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection](https://mlsanity.com/paper/2105.09509/) (2021)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping](https://mlsanity.com/paper/2310.19168/) (2023)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning](https://mlsanity.com/paper/2312.12237/) (2023)
- [Few-shot Object Detection with Refined Contrastive Learning](https://mlsanity.com/paper/2211.13495/) (2022)
- [Guided Deep Metric Learning](https://mlsanity.com/paper/2206.02029/) (2022)
- [Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification](https://mlsanity.com/paper/2209.04702/) (2022)
- [Learning to Recover Spectral Reflectance from RGB Images](https://mlsanity.com/paper/2304.02162/) (2023)
- [Unleash Model Potential: Bootstrapped Meta Self-supervised Learning](https://mlsanity.com/paper/2308.14267/) (2023)
- [Data Distribution-based Curriculum Learning](https://mlsanity.com/paper/2402.07352/) (2024)
- [TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data](https://mlsanity.com/paper/2111.04798/) (2021)
- [One-shot Learning with Absolute Generalization](https://mlsanity.com/paper/2105.13559/) (2021)
- [Learning a Discriminant Latent Space with Neural Discriminant Analysis](https://mlsanity.com/paper/2107.06209/) (2021)
- [Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity](https://mlsanity.com/paper/2207.00751/) (2022)
- [Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning](https://mlsanity.com/paper/2304.13963/) (2023)
- [Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation](https://mlsanity.com/paper/2311.04247/) (2023)
- [Quantifying Avian Morphological Evolution through Deep Representation Learning](https://mlsanity.com/paper/2602.03824/) (2026)
- [Data Distribution Valuation Using Generalized Bayesian Inference](https://mlsanity.com/paper/2604.05993/) (2026)

## Abstract (arXiv)

Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pre-training the models using a rich set of auxiliary data, followed by fine-tuning on the target FGVC task. However, the objective of pre-training does not take the target task into account, and consequently such obtained models are suboptimal for fine-tuning. To address this issue, we propose in this paper a new deep FGVC model termed MetaFGNet. Training of MetaFGNet is based on a novel regularized meta-learning objective, which aims to guide the learning of network parameters so that they are optimal for adapting to the target FGVC task. Based on MetaFGNet, we also propose a simple yet effective scheme for selecting more useful samples from the auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of our proposed method.