Semantics-Guided Contrastive Network for Zero-Shot Object Detection

Abstract

This paper presents ContrastZSD, a semantics-guided contrastive network for zero-shot object detection (ZSD). The framework improves visual-semantic alignment and mitigates the bias problem towards seen classes by incorporating region-category and region-region contrastive learning. ContrastZSD demonstrates superior performance in both ZSD and generalized ZSD tasks across PASCAL VOC and MS COCO datasets.