Enhancing zero-shot object detection with external knowledge-guided robust contrast learning

Abstract

Zero-shot object detection aims to identify objects from unseen categories not present during training. Existing methods rely on category labels to create pseudo-features for unseen categories, but they face limitations in exploring semantic information and lack robustness. To address these issues, we introduce a novel framework, EKZSD, enhancing zero-shot object detection by incorporating external knowledge and contrastive paradigms. This framework enriches semantic diversity, enhancing discriminative ability and robustness. Specifically, we introduce a novel external knowledge extraction module that leverages attribute and relationship prompts to enrich semantic information. Moreover, a novel external knowledge contrastive learning module is proposed to enhance the model’s discriminative and robust capabilities by exploring pseudo-visual features. Additionally, we use cycle consistency learning to align generated visual features with original semantic features and adversarial learning to align visual features with semantic features. Collaboratively trained with contrast learning loss, cycle consistency loss, adversarial learning loss, and classification loss, our framework outperforms superior performance on the MSCOCO and Ship-43 datasets, as demonstrated in experimental results.