Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. The global feature exploring EL or FS methods do not explore fine distinction as they ignore local details. And, the local detail exploring EL or FS methods either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, to reduce bias towards the source domain during testing, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.

翻译：嵌入式学习(EL)和特征合成(FS)是精密的GZSL方法中最受欢迎的两种类型。探索EL或FS方法的全球特征没有细微的区别,因为它们忽略了当地的细节。此外,探索EL或FS方法的地方细节忽视了直接属性指导或全球信息。因此,两种方法都效果良好。在本文件中,我们提议探索EL和FS分类的全球和直接属性监督的本地视觉特征,对精细的GZSL采用综合方式。拟议的综合网络有一个EL子网络和一个FS子网络。因此,拟议的综合网络可以用两种方式进行测试。我们提议了一个新型的两步密集关注机制,以发现自成一体的本地视觉特征。我们引入了子网络之间的新的相互学习,以利用互利信息实现优化。此外,为了在测试过程中减少对源域的偏差,我们提议根据相互的信息和转移分解的目标类来计算源目标类。我们提议的方法比基准数据集的当代方法。