  Embedding learning (EL) and feature synthesizing (FS) are two of the popular
categories of fine-grained GZSL methods. EL or FS using global features cannot
discriminate fine details in the absence of local features. On the other hand,
EL or FS methods exploiting local features 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, we propose to compute source-target class similarity
based on mutual information and transfer-learn the target classes to reduce
bias towards the source domain during testing. We demonstrate that our proposed
method outperforms contemporary methods on benchmark datasets.
