Self-supervised Cross-stage Regional Contrastive Learning for Object Detection

Abstract

Cross-stage object similarity is a vital property of generic supervised object detectors, which maintains similar feature responses to the same object across feature maps of different intermediate stages of the backbone network. Since an object can be predicted by multiple stages, this similarity is beneficial for accurate object classification and localization. Inspired by this property, we introduce Cross-stage regional Contrastive Learning (CrossCL) to learn the cross-stage object similarity during the model pre-training. Since labels are unavailable in self-supervised learning, we treat the regions sharing the same position in different stages as the same object and constrain them to have similar feature responses across stages to achieve cross-stage object similarity. The learned feature representations of CrossCL share a similar property with supervised detectors, thus showing strong transfer capability to object detection tasks. Besides, we also provide in-depth discussions, ablation studies, and visualizations to understand better how CrossCL works. Code is available at https://github.com/yanjk3/CrossCL.