A Semantic Encoding Out-of-Distribution Classifier for Generalized Zero-Shot Learning

Abstract

Generalized zero-shot learning (GZSL) poses a challenging problem in that it aims to recognize both seen classes that have appeared in the training stage and unseen classes that have not appeared during training. By utilizing a gating mechanism as the binary classifier, gating methods can decompose GZSL into a conventional ZSL problem and a supervision learning task, thereby leading to outstanding performance by GZSL. However, unseen classes contain many confusing visual samples that distribute too close to the seen class boundaries and are prone to misclassification. To solve this problem, we propose a novel semantic encoding out-of-distribution classifier (SE-OOD) for GZSL. Our method first utilizes semantically consistent mapping to project all the visual samples to their corresponding semantic attributes. Then, both the projected visual samples and original semantic attributes are encoded to their latent representations for distribution alignment. After separating the unseen samples from seen samples in the learned latent space, two domain classifiers are adopted to perform ZSL and supervised classification tasks. Extensive experiments are conducted on four benchmarks, and the results show that our proposed SE-OOD can outperform the state-of-the-arts by a large margin.