  Compositional Zero-Shot Learning (CZSL) aims to recognize unseen
attribute-object pairs based on a limited set of observed examples. Current
CZSL methodologies, despite their advancements, tend to neglect the distinct
specificity levels present in attributes. For instance, given images of sliced
strawberries, they may fail to prioritize `Sliced-Strawberry' over a generic
`Red-Strawberry', despite the former being more informative. They also suffer
from ballooning search space when shifting from Close-World (CW) to Open-World
(OW) CZSL. To address the issues, we introduce the Context-based and
Diversity-driven Specificity learning framework for CZSL (CDS-CZSL). Our
framework evaluates the specificity of attributes by considering the diversity
of objects they apply to and their related context. This novel approach allows
for more accurate predictions by emphasizing specific attribute-object pairs
and improves composition filtering in OW-CZSL. We conduct experiments in both
CW and OW scenarios, and our model achieves state-of-the-art results across
three datasets.
