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.

翻译：组合式零样本学习旨在从已见组合中学习视觉概念（即属性和对象），并将概念知识结合到未见组合中。CZSL的关键在于学习属性-对象组合的解耦。为此，我们提出利用交叉注意力作为组合解耦器来学习解耦的概念嵌入。例如，若要识别未见组合“黄色花朵”，我们可以分别从不同黄色对象和不同花朵中学习属性概念“黄色”和对象概念“花朵”。为进一步约束解耦器专注于学习目标概念，我们在注意力层面引入正则化。具体而言，我们采用地球移动距离作为交叉注意力模块中的特征相似性度量。此外，得益于概念解耦，我们改进了推理过程，并通过结合多个概念概率来调整预测分数。在三个CZSL基准数据集上的综合实验表明，我们的方法在封闭世界和开放世界设定下均显著优于先前工作，达到了新的最优水平。