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Few-Shot Learning is the challenge of training a model with only a smallamount 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 largerdataset, we can teach these algorithms to solve new, unseen tasks. Thisdocument reports my work on meta-learning algorithms for Few-Shot ComputerVision. This work was done during my internship at Sicara, a French companybuilding image recognition solutions for businesses. It contains: 1. anextensive review of the state-of-the-art in few-shot computer vision; 2. abenchmark of meta-learning algorithms for few-shot image classification; 3. theintroduction to a novel meta-learning algorithm for few-shot object detection,which is still in development.

Meta-learning algorithms for Few-Shot Computer Vision