The key challenge of zero-shot learning (ZSL) is how to infer the latent
semantic knowledge between visual and attribute features on seen classes, and
thus achieving a desirable knowledge transfer to unseen classes. Prior works
either simply align the global features of an image with its associated class
semantic vector or utilize unidirectional attention to learn the limited latent
semantic representations, which could not effectively discover the intrinsic
semantic knowledge e.g., attribute semantics) between visual and attribute
features. To solve the above dilemma, we propose a Mutually Semantic
Distillation Network (MSDN), which progressively distills the intrinsic
semantic representations between visual and attribute features for ZSL. MSDN
incorporates an attribute$\rightarrow$visual attention sub-net that learns
attribute-based visual features, and a visual$\rightarrow$attribute attention
sub-net that learns visual-based attribute features. By further introducing a
semantic distillation loss, the two mutual attention sub-nets are capable of
learning collaboratively and teaching each other throughout the training
process. The proposed MSDN yields significant improvements over the strong
baselines, leading to new state-of-the-art performances on three popular
challenging benchmarks, i.e., CUB, SUN, and AWA2. Our codes have been available
at: https://github.com/shiming-chen/MSDN.

本文提出了一个相互语义蒸馏网络（MSDN），通过学习属性感知的视觉特征和视觉感知的属性特征，不断地提炼视觉和属性之间的内在语义表示来解决零样本学习中的潜在语义知识推理问题，并在三个数据集上实现了新的最先进性能。