Few-Shot Object Detection with Model Calibration

Abstract

Few-shot object detection (FSOD) targets at transferring knowledge from known to unknown classes to detect objects of novel classes. However, previous works ignore the model bias problem inherent in the transfer learning paradigm. Such model bias causes overfitting toward the training classes and destructs the well-learned transferable knowledge. In this paper, we pinpoint and comprehensively investigate the model bias problem in FSOD models and propose a simple yet effective method to address the model bias problem with the facilitation of model calibrations in three levels: 1) Backbone calibration to preserve the well-learned prior knowledge and relieve the model bias toward base classes, 2) RPN calibration to rescue unlabeled objects of novel classes and, 3) Detector calibration to prevent the model bias toward a few training samples for novel classes. Specifically, we leverage the overlooked classification dataset to facilitate our model calibration procedure, which has only been used for pre-training in other related works. We validate the effectiveness of our model calibration method on the popular Pascal VOC and MS COCO datasets, where our method achieves very promising performance. Codes are released at https://github.com/fanq15/FewX.