This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to explore the three key properties required by the task --- relation-aware, consistent, and decoupled --- to learn rich and robust features for primitive concepts that compose attribute-object pairs. To this end, we propose the Blocked Message Passing Network (BMP-Net). The model consists of two modules. The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images. A message passing mechanism is used in the concept module to capture the relations between primitive concepts. Furthermore, to prevent the model from being biased towards seen composite concepts and reduce the entanglement between attributes and objects, we propose a blocking mechanism that equalizes the information available to the model for both seen and unseen concepts. Extensive experiments and ablation studies on two benchmarks show the efficacy of the proposed model.

本文提出了一种模型，使用基于阻塞消息传递网络的方法来识别由属性和对象组成的复合概念的图像，特别是用于未经过模型训练的复合概念。实验证明该模型是具有关系感知、一致性和分离三个关键属性的，利用这些属性学习了由基本概念组成的丰富而强健的特征。

基于关系感知的组合式零样本学习用于属性-物品对识别