CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection

arXiv:2403.19278v1 Announce Type: new
Abstract: Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However, a fundamental issue arises from the class imbalance in the labelled training set, which can result in inaccurate pseudo-labels. The relationship between classes, especially where one class is a majority and the other minority, has a large impact on class bias. We propose …

abstract adapt annotated data arxiv class cs.cv data detection domain domains dynamics framework gap however issue object semi-supervised set training type