Zero-Shot Object Detection with Partitioned Contrastive Feature Alignment
Haohe Li, Chong Wang, Shenghao Yu, Zheng Huo, Yujie Zheng, Jiangbo Qian · 2024
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. Experimental results on the MS-COCO dataset show the superiority of the proposed model. Our code is available at: https://github.com/lihh1023/PCFA-ZSD.