CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning
<|reference_start|>CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning: 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.<|reference_end|>
@article{zhang2024cscnet:,
    title={CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot
  Learning},
    author={Yanyi Zhang, Qi Jia, Xin Fan, Yu Liu, Ran He},
    journal={arXiv preprint arXiv:2403.05924},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05924},
    primaryClass={cs.CV}
}