Generalized Zero-shot learning (GZSL) for object recognition is to solve the problem of recognizing samples in both seen and unseen classes, while only having seen classes in training. As labeling abundant examples for all kinds of classes in realistic scenarios is costly and impractical, GZSL has become a novel and hot field of research in recent years. Most previous methods tried to learn a fixed one-directional mapping, either from visual to semantic features, or from semantic to visual features. However, recently, cross mapping between visual and semantic features has achieved good results and many methods come up based on this idea. In this paper, we propose a novel model. It is Multi-channel Gaussian Mixture VAE(MCGM-VAE), which introduces Gaussian mixture model to our multi-modal VAE with multiple channels. These channels are of different weight coefficients following with channel-weight layers, so as to produce a Gaussian mixture distribution. Then the latent space could be generated from it. We evaluate our method on several benchmark databases, i.e. CUB, SUN, AWA1, AWA2, aPY and prove our approach outperforms state-of-the-art methods. Through the experimental data analysis, the impact of some hyperparameters on the experimental performance is further analyzed.

中文翻译：

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具有多通道高斯混合 VAE 的广义零样本学习

用于对象识别的广义零样本学习（GZSL）是为了解决在可见和不可见类中识别样本的问题，而只在训练中看到类。由于在现实场景中为各种类别标记大量示例成本高昂且不切实际，因此 GZSL 已成为近年来的一个新颖而热门的研究领域。大多数以前的方法试图学习一个固定的单向映射，从视觉到语义特征，或者从语义到视觉特征。然而，最近视觉和语义特征之间的交叉映射取得了很好的效果，并且基于这种思想提出了许多方法。在本文中，我们提出了一种新颖的模型。它是多通道高斯混合VAE（MCGM-VAE），它将高斯混合模型引入我们的多通道多模态VAE。这些通道具有不同的权重系数，随后是通道权重层，从而产生高斯混合分布。然后可以从中生成潜在空间。我们在几个基准数据库上评估我们的方法，即 CUB、SUN、AWA1、AWA2、aPY，并证明我们的方法优于最先进的方法。通过实验数据分析，进一步分析了一些超参数对实验性能的影响。