Meta-learning algorithms for Few-Shot Computer Vision
This work addresses the challenge of training models with limited data for businesses using image recognition, but it appears incremental as it builds on existing meta-learning approaches.
The paper tackles the problem of few-shot learning in computer vision by reviewing state-of-the-art methods and benchmarking meta-learning algorithms for image classification, while introducing a novel algorithm for few-shot object detection that is still in development.
Few-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we can teach these algorithms to solve new, unseen tasks. This document reports my work on meta-learning algorithms for Few-Shot Computer Vision. This work was done during my internship at Sicara, a French company building image recognition solutions for businesses. It contains: 1. an extensive review of the state-of-the-art in few-shot computer vision; 2. a benchmark of meta-learning algorithms for few-shot image classification; 3. the introduction to a novel meta-learning algorithm for few-shot object detection, which is still in development.