Weighted alignment-based multi-source domain adaptation for object detection

Beyond single-source domain adaption (DA) for object detection, multi-source domain adaptation for object detection is another challenge because the authors should solve the multiple domain shifts between the source and target domains as well as between multiple source domains. In this letter, the authors propose a novel multi-source domain adaptation via weighted alignment for object detection where the authors adopt a teacher-student framework. The authors first propose the weighted multiple binary discriminator (MBD) to align the multiple domains considering individual domain shifts. The authors also design the weighted class balance loss (CBL) that aligns the different weights to efficiently learn the detection model even if the number of objects is not balanced in an image under the teacher-student learning scheme. The authors empirically prove the superiority of our method on widely used benchmarks such as Cityscapes, KITTI, and BDD100k datasets.