Improving Cross-Domain Detection with Self-Supervised Learning

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Publication Date: 6/19/2023

Event: CVPR 2023, 2nd Workshop on Learning with Limited Labelled Data for Image and Video Understanding

Reference: pp. 4745-4754, 2023

Authors: Kai Li, NEC Laboratories America, Inc., Northeastern University; Curtis Wigington, Adobe Inc.; Chris Tensmeyer, Adobe Inc.; Vlad I. Morariu, Adobe Inc.; Handong Zhao, Adobe Inc.; Varun Manjunatha, Adobe Inc.; Nikolaos Barmpalios, Adobe Inc.; Yun Fu, Northeastern University

Abstract: Cross-Domain Detection (XDD) aims to train a domain-adaptive object detector using unlabeled images from a target domain and labeled images from a source domain. Existing approaches achieve this either by aligning the feature maps or the region proposals from the two domains, or by transferring the style of source images to that of target images. In this paper, rather than proposing another method following the existing lines, we introduce a new framework complementary to existing methods. Our framework unifies some popular Self-Supervised Learning (SSL) techniques (e.g., rotation angle prediction, strong/weak data augmentation, mean teacher modeling) and adapts them to the XDD task. Our basic idea is to leverage the unsupervised nature of these SSL techniques and apply them simultaneously across domains (source and target) and models (student and teacher). These SSL techniques can thus serve as shared bridges that facilitate knowledge transfer between domains. More importantly, as these techniques are independently applied in each domain, they are complementary to existing domain alignment techniques that relies on interactions between domains (e.g., adversarial alignment). We perform extensive analyses on these SSL techniques and show that they significantly improve the performance of existing methods. In addition, we reach comparable or even better performance than the state-of-the-art methods when integrating our framework with an old well-established method.

Publication Link: https://openaccess.thecvf.com/content/CVPR2023W/L3D-IVU/html/Li_Improving_Cross-Domain_Detection_With_Self-Supervised_Learning_CVPRW_2023_paper.html