Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (SSRN Electronic Journal)

[CVPR 2022] Official Pytorch Implementation for "Spatio-temporal Relation Modeling for Few-shot Action Recognition". SOTA Results for Few-shot Action Recognition

Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)

[PR22, Highly Cited Paper] Learning Attention-Guided Pyramidal Features for Few-shot Fine-grained Recognition

VLG: General Video Recognition with Web Textual Knowledge (https://arxiv.org/abs/2212.01638)

[IEEE ICIP (2021)] Coupled Patch Similarity Network For One-Shot Fine-Grained Image Recognition

SmartFew is your swiss knife for semi-supervised structuring of unlabeled data using Few Shot Learning.

Learning to learn for Personalised Human Activity Recognition

Library to recognise and classify faces.

Code for "Improved Few-Shot Visual Classification"