Variational Information Bottleneck for Cross Domain Object Detection

Abstract

Cross domain object detection leverages a labeled source domain to learn an object detector which performs well in a novel unlabeled target domain. Most existing works mainly align the distribution utilizing the entire image knowledge ignoring the obstacles of task-uncorrelated information to alleviate the domain discrepancy. To tackle this issue, we propose a novel module called Variational Instance Disentanglement (VID) based on information theory which aims to decouple the information of task-correlated while filtering out the task-uncorrelated factors at the instance level. Notably, the proposed VID can be used as a plug-and-play module without bringing extra network parameter cost. We equip it with adversarial network and self-training network forming Variational Instance Disentanglement Adversarial Network (VIDAN) and Variational Instance Disentanglement Self-training Network (VIDSN), respectively. Extensive experiments on multiple widely-used scenarios show that the proposed method improves the performance of the popular frameworks and outperforms state-of-the-art methods.