A Simple Approach for Zero-Shot Learning based on Triplet Distribution Embeddings

TL;DR

This paper introduces a simple distribution-based approach for zero-shot learning that models class and image embeddings as Gaussian distributions, improving expressivity and achieving competitive results.

Contribution

It proposes using distribution embeddings with triplet constraints for ZSL, enhancing modeling of intra-class variability over traditional vector methods.

Findings

Achieves competitive results on benchmark datasets.

Effective in both traditional ZSL and GZSL settings.

Models embeddings as Gaussian distributions for better expressivity.

Abstract

Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to recognize unseen classes without labeled training data by exploiting semantic information, which contains knowledge between seen and unseen classes. Existing ZSL methods mainly use vectors to represent the embeddings to the semantic space. Despite the popularity, such vector representation limits the expressivity in terms of modeling the intra-class variability for each class. We address this issue by leveraging the use of distribution embeddings. More specifically, both image embeddings and class embeddings are modeled as Gaussian distributions, where their similarity relationships are preserved through the use of triplet constraints. The key intuition which guides our approach is that for each image, the embedding of the correct class label should be closer than that of any other class label. Extensive experiments on multiple benchmark data sets show that the proposed method achieves highly competitive results for both traditional ZSL and more challenging Generalized Zero-Shot Learning (GZSL) settings.