Generalized Zero-Shot Learning via Aligned Variational Autoencoders

Edgar Schonfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 54-57

Abstract


Most approaches in generalized zero-shot learning rely on cross-modal mapping between an image feature space and a class embedding space or on generating artificial image features. However, learning a shared cross-modal embedding by aligning the latent spaces of modality-specific autoencoders is shown to be promising in (generalized) zero-shot learning. While following the same direction, we also take artificial feature generation one step further and propose a model where a shared latent space of image features and class embeddings is learned by aligned variational autoencoders, for the purpose of generating latent features to train a softmax classifier. We evaluate our learned latent features on conventional benchmark datasets and establish a new state of the art on generalized zero-shot learning. Moreover, our results on ImageNet with various zero-shot splits show that our latent features generalize well in large-scale settings. The extended version of this work is accepted for publication at CVPR 2019[16].

Related Material


[pdf]
[bibtex]
@InProceedings{Schonfeld_2019_CVPR_Workshops,
author = {Schonfeld, Edgar and Ebrahimi, Sayna and Sinha, Samarth and Darrell, Trevor and Akata, Zeynep},
title = {Generalized Zero-Shot Learning via Aligned Variational Autoencoders},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}