Compositional zero-shot learning (CZSL) aims at learning visual concepts
(i.e., attributes and objects) from seen compositions and combining concept
knowledge into unseen compositions. The key to CZSL is learning the
disentanglement of the attribute-object composition. To this end, we propose to
exploit cross-attentions as compositional disentanglers to learn disentangled
concept embeddings. For example, if we want to recognize an unseen composition
"yellow flower", we can learn the attribute concept "yellow" and object concept
"flower" from different yellow objects and different flowers respectively. To
further constrain the disentanglers to learn the concept of interest, we employ
a regularization at the attention level. Specifically, we adapt the earth
mover's distance (EMD) as a feature similarity metric in the cross-attention
module. Moreover, benefiting from concept disentanglement, we improve the
inference process and tune the prediction score by combining multiple concept
probabilities. Comprehensive experiments on three CZSL benchmark datasets
demonstrate that our method significantly outperforms previous works in both
closed- and open-world settings, establishing a new state-of-the-art.

该论文提出了一种使用交叉注意力作为组合分离器来学习分离概念嵌入的方法，使用地球移动距离作为跨注意力模块中的特征相似度度量，在多个概念概率的基础上提高推理结果，并在三个 CZSL 基准数据集上进行的综合实验表明，该方法在闭合和开放世界设置下都显著优于以前的方法，建立了一个新的最先进技术。