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.

中文翻译：

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用于广义零样本学习的语义编码分布外分类器


广义零样本学习（GZSL）提出了一个具有挑战性的问题，因为它的目标是识别训练阶段中出现过的可见类和训练期间未出现过的未见类。通过利用门控机制作为二元分类器，门控方法可以将 GZSL 分解为传统的 ZSL 问题和监督学习任务，从而使 GZSL 具有出色的性能。然而，看不见的类包含许多令人困惑的视觉样本，这些样本分布得太接近可见的类边界，并且很容易出现错误分类。为了解决这个问题，我们为 GZSL 提出了一种新颖的语义编码分布外分类器（SE-OOD）。我们的方法首先利用语义一致的映射将所有视觉样本投影到其相应的语义属性。然后，投影的视觉样本和原始语义属性都被编码为其潜在表示，以进行分布对齐。在学习到的潜在空间中将未见样本与已见样本分离后，采用两个域分类器来执行 ZSL 和监督分类任务。在四个基准上进行了大量的实验，结果表明我们提出的 SE-OOD 可以大幅优于最先进的技术。