Incremental few-shot object detection via knowledge transfer

Hangtao Feng, Lu Zhang, Xu Yang, Zhiyong Liu

Abstract: Highlights•We focus on the challenging Incremental Few-Shot Object Detection (iFSD) problem and propose a feature transfer module to transfer the learned information from base features to the novel ones.•We propose a dual-stream network to transfer the knowledge learned from the base class to the novel class from the two levels of weight and network structure.•Extensive experiments are conducted on the challenging PASCAL VOC and MS COCO dataset under iFSD setting, demonstrating the effectiveness of our method.•The proposed method outperforms the existing methods.