A domain adaptive object detector aims to adapt itself to unseen domains that
may contain variations of object appearance, viewpoints or backgrounds. Most
existing methods adopt feature alignment either on the image level or instance
level. However, image-level alignment on global features may tangle
foreground/background pixels at the same time, while instance-level alignment
using proposals may suffer from the background noise. Different from existing
solutions, we propose a domain adaptation framework that accounts for each
pixel via predicting pixel-wise objectness and centerness. Specifically, the
proposed method carries out center-aware alignment by paying more attention to
foreground pixels, hence achieving better adaptation across domains. We
demonstrate our method on numerous adaptation settings with extensive
experimental results and show favorable performance against existing
state-of-the-art algorithms.

本文提出了一个基于像素级目标度和中心化对齐的域自适应目标检测器，以优化跨域适配，实验结果证明了其相对于现有算法的优越性。