  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.
