  Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives
(attributes and objects) from seen compositions and recognize unseen
attribute-object compositions. Existing CZSL datasets focus on single
attributes, neglecting the fact that objects naturally exhibit multiple
interrelated attributes. Real-world objects often possess multiple interrelated
attributes, and current datasets' narrow attribute scope and single attribute
labeling introduce annotation biases, undermining model performance and
evaluation. To address these limitations, we introduce the Multi-Attribute
Composition (MAC) dataset, encompassing 18,217 images and 11,067 compositions
with comprehensive, representative, and diverse attribute annotations. MAC
includes an average of 30.2 attributes per object and 65.4 objects per
attribute, facilitating better multi-attribute composition predictions. Our
dataset supports deeper semantic understanding and higher-order attribute
associations, providing a more realistic and challenging benchmark for the CZSL
task. We also develop solutions for multi-attribute compositional learning and
propose the MM-encoder to disentangling the attributes and objects.
