  Compositional Zero-Shot Learning (CZSL) aims to train models to recognize
novel compositional concepts based on learned concepts such as attribute-object
combinations. One of the challenges is to model attributes interacted with
different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is
different. As a solution, we provide analysis and argue that attributes are
conditioned on the recognized object and input image and explore learning
conditional attribute embeddings by a proposed attribute learning framework
containing an attribute hyper learner and an attribute base learner. By
encoding conditional attributes, our model enables to generate flexible
attribute embeddings for generalization from seen to unseen compositions.
Experiments on CZSL benchmarks, including the more challenging C-GQA dataset,
demonstrate better performances compared with other state-of-the-art approaches
and validate the importance of learning conditional attributes. Code is
available at https://github.com/wqshmzh/CANet-CZSL
