Few-shot object detection with semantic enhancement and semantic prototype contrastive learning

作者：

Highlights：

• Semantic knowledge strengthens the expression of visual information.

• Semantic prototype contrastive enables the classifier to learn a more discriminative embedding space.

• Semantic margin facilitates the separation of features belonging to similar classes.

• The proposed method achieves state-of-the-art few-shot object detection performance.

摘要

•Semantic knowledge strengthens the expression of visual information.•Semantic prototype contrastive enables the classifier to learn a more discriminative embedding space.•Semantic margin facilitates the separation of features belonging to similar classes.•The proposed method achieves state-of-the-art few-shot object detection performance.

论文关键词：Few-shot learning,Object detection,Word embedding,Supervised contrastive learning,Cross-attention

论文评审过程：Received 24 March 2022, Revised 7 July 2022, Accepted 8 July 2022, Available online 13 July 2022, Version of Record 25 July 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109411