Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning
This work improves accuracy in compositional zero-shot learning for computer vision applications, though it appears incremental as it builds on existing methodologies.
The paper tackles the problem of recognizing unseen attribute-object pairs in Compositional Zero-Shot Learning by addressing neglected specificity levels in attributes and ballooning search space in Open-World scenarios, achieving state-of-the-art results across three datasets.
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.