Compared to conventional zero-shot learning (ZSL) where recognising unseen
classes is the primary or only aim, the goal of generalized zero-shot learning
(GZSL) is to recognise both seen and unseen classes. Most GZSL methods
typically learn to synthesise visual representations from semantic information
on the unseen classes. However, these types of models are prone to overfitting
the seen classes, resulting in distribution overlap between the generated
features of the seen and unseen classes. The overlapping region is filled with
uncertainty as the model struggles to determine whether a test case from within
the overlap is seen or unseen. Further, these generative methods suffer in
scenarios with sparse training samples. The models struggle to learn the
distribution of high dimensional visual features and, therefore, fail to
capture the most discriminative inter-class features. To address these issues,
in this paper, we propose a novel framework that leverages dual variational
autoencoders with a triplet loss to learn discriminative latent features and
applies the entropy-based calibration to minimize the uncertainty in the
overlapped area between the seen and unseen classes. Specifically, the dual
generative model with the triplet loss synthesises inter-class discriminative
latent features that can be mapped from either visual or semantic space. To
calibrate the uncertainty for seen classes, we calculate the entropy over the
softmax probability distribution from a general classifier. With this approach,
recognising the seen samples within the seen classes is relatively
straightforward, and there is less risk that a seen sample will be
misclassified into an unseen class in the overlapped region. Extensive
experiments on six benchmark datasets demonstrate that the proposed method
outperforms state-of-the-art approaches.

提出一种新的框架，利用双重变分自编码器和三元组损失学习区分性潜在特征，并应用基于熵的校准来最小化见和未见类之间重叠区域的不确定性，在六个基准数据集上进行的实验表明，该方法胜过现有的最先进方法。