CRPN: DISTINGUISH NOVEL CATEGORIES VIA CLASS-RELEVANT REGION PROPOSAL NETWORK FOR FEW-SHOT OBJECT DETECTION

Few-shot object detection (FSOD) has attracted more attention in computer vision, where only very few training examples are presented during model learning process. A commonly-overlooked issue in FSOD is that novel classes are usually classified as background clutters in the pre-training process. Another difficulty of FSOD is that the detection performance degrades especially under higher IoU thresholds since previous deep metric learning (DML) requires frozen region proposals without class-relevant box regression. In this work, we propose a Class-relevant Region Proposal Network (CRPN). The CRPN can derive network parameters for novel classes from pre-trained convolution kernels according to their feature similarity, which is used to eliminate the above mentioned adverse effects and improve the performance of few-shot object detection. The proposed CPRN is able to kill two birds with one stone and has two main contributions: (1) transfer a region proposal network pre-trained on base classes to novel classes; (2) perform class-dependent bounding-box regression which previous DML classifier lacks. For experimental testing, we achieve 12.7% AP75 in MS COCO dataset and 28.6% AP75 in ImageNet2015 dataset under the few-shot setting introduced by previous works, which exceeds the state-of-the-art by a certain margin.