@inproceedings{
fereydooni2023divide,
title={Divide and Conquer: Two-Level Problem Remodeling for Large-Scale Few-Shot Learning},
author={Mohamadreza Fereydooni and Hosein Hasani and Ali Razghandi and Mahdieh Soleymani Baghshah},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=ED7E1fUAk2}
}
Few-shot learning methods have achieved notable performance in recent years. However, few-shot learning in large-scale settings with hundreds of classes is still challenging.
In this paper, we tackle the problems of large-scale few-shot learning by taking advantage of pre-trained foundation models. We recast the original problem in two levels with different granularity. At the coarse-grained level, we introduce a novel object recognition approach with robustness to sub-population shifts. At the fine-grained level,  generative experts are designed for few-shot learning, specialized for different superclasses.
A Bayesian schema is considered to combine coarse-grained information with fine-grained predictions in a winner-takes-all fashion.
Extensive experiments on large-scale datasets and different architectures show that the proposed method is both effective and efficient besides its simplicity and natural problem remodeling. The code is publicly available at https://github.com/mohamadreza99/divide_and_conquer.
Divide and Conquer: Two-Level Problem Remodeling for Large-Scale Few-Shot Learning
Mohamadreza Fereydooni Hosein Hasani Ali Razghandi Mahdieh Soleymani Baghshah