  Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new
compositions of seen attributes and objects. In OW-CZSL, methods built on the
conventional closed-world setting degrade severely due to the unconstrained OW
test space. While previous works alleviate the issue by pruning compositions
according to external knowledge or correlations in seen pairs, they introduce
biases that harm the generalization. Some methods thus predict state and object
with independently constructed and trained classifiers, ignoring that
attributes are highly context-dependent and visually entangled with objects. In
this paper, we propose a novel Distilled Reverse Attention Network to address
the challenges. We also model attributes and objects separately but with
different motivations, capturing contextuality and locality, respectively. We
further design a reverse-and-distill strategy that learns disentangled
representations of elementary components in training data supervised by reverse
attention and knowledge distillation. We conduct experiments on three datasets
and consistently achieve state-of-the-art (SOTA) performance.
