Zero-Shot Object Detection with Partitioned Contrastive Feature Alignment

How to properly align the extracted visual features with certain semantic embeddings of unseen objects is crucial to the problem of Zero-Shot Object Detection (ZSD). To give a better guess of those unseen visual features, a partitioned contrast strategy is proposed in this paper to train the visual and attribute feature alignment networks. To be specific, four types of contrast are considered, including the visual-to-visual, visual-to-attribute, attribute-to-visual and attribute-to-attribute contrasts. Combining with two cross-batch memory banks of the visual features and unseen attribute features, it is effective to adjust the alignment rules for unseen visual features. Although the visual features of the unseen classes are missing during training, the common projection rules for visual and attribute features are learned by emphasizing the contrast involving unseen attribute features. Experimental results on the MS-COCO dataset show the superiority of the proposed model. Our code and models are publicly available at: https://github.com/lihh1023/PCFA-ZSD.