Contrastive Class-Specific Encoding for Few-Shot Object Detection

Dizhong Lin, Ying Fu, Xin Wang, Shu Hu, Bin Zhu, Qi Song, Xi Wu, Siwei Lyu

Abstract: In this paper, we propose a new few-shot object detection (FSOD) framework that introduces a new contrastive branch to extract the class representation of images, which improves the generalization performance of the detection model for novel classes. Additionally, we investigate the effectiveness of both self-supervised and supervised contrastive losses for class-specific encoding in our framework. Experimental results on the benchmark datasets indicate that our proposed method archives the state-of-the-art performance compared with existing FSOD methods.