  The prevalent approach in domain adaptive object detection adopts a two-stage
architecture (Faster R-CNN) that involves a number of hyper-parameters and
hand-crafted designs such as anchors, region pooling, non-maximum suppression,
etc. Such architecture makes it very complicated while adopting certain
existing domain adaptation methods with different ways of feature alignment. In
this work, we adopt a one-stage detector and design DA-DETR, a simple yet
effective domain adaptive object detection network that performs inter-domain
alignment with a single discriminator. DA-DETR introduces a hybrid attention
module that explicitly pinpoints the hard-aligned features for simple yet
effective alignment across domains. It greatly simplifies traditional domain
adaptation pipelines by eliminating sophisticated routines that involve
multiple adversarial learning frameworks with different types of features.
Despite its simplicity, extensive experiments show that DA-DETR demonstrates
superior accuracy as compared with highly-optimized state-of-the-art
approaches.
