AOGN-CZSL: An Attribute- and Object-Guided Network for Compositional Zero-Shot Learning

Abstract

Humans are able to readily acquire knowledge about unfamiliar and unknown objects. However, it is extremely challenging for artificial intelligence to achieve this skill. With the rapid development of artificial intelligence, compositional zero-shot learning (CZSL) can generalize unseen compositions by learning prior knowledge of seen attributes and object compositions during training. Although existing composition-based and relationship-based methods show great potential for addressing this challenge, they still exhibit some limitations. Composition-based methods often ignore the intrinsic correlations between attributes, objects, and images, which may lead the model to perform poorly when it is generalized to unseen compositions. Some relationship-based methods can better capture the relationships between attributes, objects, and images but may overlook the interdependencies between distinct attributes and objects. Therefore, the advantages of the composition-based and relationship-based methods are combined, and a new method is proposed for learning attribute and object dependencies (AOGN-CZSL). AOGN-CZSL learns the dependencies between different attributes or objects. It also learns all attributes and objects simultaneously. Different from traditional composition-based methods, it typically address each attribute-object compositions separately. Moreover, unlike general relation-based approaches, this paper adopts learned textual and visual modality features of attributes and objects for attribute scoring and object scoring, respectively. The code and dataset are available at: https://github.com/ybyangjing/AOGN-CZSL.