Revisiting Metric Learning for Few-Shot Image Classification

TL;DR

This paper revisits classical metric learning techniques, extending them into deep K-tuplet networks for few-shot image classification, demonstrating superior generalization and performance across multiple datasets.

Contribution

It introduces a deep K-tuplet network based on triplet networks, emphasizing feature embedding and relationship among samples for improved few-shot learning.

Findings

Outperforms existing metric-based methods on miniImageNet.

Generalizes well to unseen datasets like Caltech-101 and Stanford Dogs.

Significantly improves few-shot classification accuracy.

Abstract

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes.