Skip to yearly menu bar Skip to main content


Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning

Yun Li · Zhe Liu · Hang Chen · Lina Yao

Arch 4A-E Poster #236
[ ]
Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

Live content is unavailable. Log in and register to view live content