Generalized Zero-Shot Learning (GZSL) aims to recognize images from both seen and unseen categories. Most GZSL methods typically learn to synthesize CNN visual features for the unseen classes by leveraging entire semantic information, e.g., tags and attributes, and the visual features of the seen classes. Within the visual features, we define two types of features that semantic-consistent and semantic-unrelated to represent the characteristics of images annotated in attributes and less informative features of images respectively. Ideally, the semantic-unrelated information is impossible to transfer by semantic-visual relationship from seen classes to unseen classes, as the corresponding characteristics are not annotated in the semantic information. Thus, the foundation of the visual feature synthesis is not always solid as the features of the seen classes may involve semantic-unrelated information that could interfere with the alignment between semantic and visual modalities. To address this issue, in this paper, we propose a novel feature disentangling approach based on an encoder-decoder architecture to factorize visual features of images into these two latent feature spaces to extract corresponding representations. Furthermore, a relation module is incorporated into this architecture to learn semantic-visual relationship, whilst a total correlation penalty is applied to encourage the disentanglement of two latent representations. The proposed model aims to distill quality semantic-consistent representations that capture intrinsic features of seen images, which are further taken as the generation target for unseen classes. Extensive experiments conducted on seven GZSL benchmark datasets have verified the state-of-the-art performance of the proposal.

本文针对广义零样本学习任务，提出了一种新颖的语义分离框架，使用条件VAE将未见过的类别的视觉特征分解为与语义一致、不相关的潜在向量，并且通过相关性惩罚和关系网络确保两个分解表示之间的独立性和语义一致性。在四个广义零样本学习基准数据集上的实验表明，由该框架分离出的语义一致特征在规范化和广义零样本学习任务中更具普适性。

泛零样本学习的语义分离