  Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon
base models which consider only a single class attribute vector representation
over the entire image. This is an oversimplification of the process of novel
category recognition, where different regions of the image may have properties
from different seen classes and thus have different predominant attributes.
With this in mind, we take a fundamentally different approach: a pre-trained
Vision-Language detector (VINVL) sensitive to attribute information is employed
to efficiently obtain region features. A learned function maps the region
features to region-specific attribute attention used to construct class part
prototypes. We conduct experiments on a popular GZSL benchmark consisting of
the CUB, SUN, and AWA2 datasets where our proposed Part Prototype Network (PPN)
achieves promising results when compared with other popular base models.
Corresponding ablation studies and analysis show that our approach is highly
practical and has a distinct advantage over global attribute attention when
localized proposals are available.
