Few-Shot Image Classification
201 papers with code • 88 benchmarks • 23 datasets
Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
( Image credit: Learning Embedding Adaptation for Few-Shot Learning )
Libraries
Use these libraries to find Few-Shot Image Classification models and implementationsDatasets
Subtasks
Most implemented papers
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.
Meta-Learning with Differentiable Convex Optimization
We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
Meta-Learning with Implicit Gradients
By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer.
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
Towards a Neural Statistician
We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels.
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
Low-shot Visual Recognition by Shrinking and Hallucinating Features
Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence.