CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning

• URL: http://arxiv.org/abs/2403.05924v2
• Date: Wed, 13 Mar 2024 11:36:43 GMT
• Title: CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
• Authors: Yanyi Zhang, Qi Jia, Xin Fan, Yu Liu, Ran He
• Abstract summary: Attribute and object (A-O) disentanglement is a fundamental and critical problem for Compositional Zero-shot Learning (CZSL) We propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet)
• Score: 62.090051975043544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attribute and object (A-O) disentanglement is a fundamental and critical problem for Compositional Zero-shot Learning (CZSL), whose aim is to recognize novel A-O compositions based on foregone knowledge. Existing methods based on disentangled representation learning lose sight of the contextual dependency between the A-O primitive pairs. Inspired by this, we propose a novel A-O disentangled framework for CZSL, namely Class-specified Cascaded Network (CSCNet). The key insight is to firstly classify one primitive and then specifies the predicted class as a priori for guiding another primitive recognition in a cascaded fashion. To this end, CSCNet constructs Attribute-to-Object and Object-to-Attribute cascaded branches, in addition to a composition branch modeling the two primitives as a whole. Notably, we devise a parametric classifier (ParamCls) to improve the matching between visual and semantic embeddings. By improving the A-O disentanglement, our framework achieves superior results than previous competitive methods.

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