论文标题
每个像素都很重要：域自适应对象检测器的中心感知功能对齐
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector
论文作者
论文摘要
域自适应对象检测器的目的是适应可能包含对象外观，观点或背景的不同域。大多数现有方法在图像级别或实例级别上采用特征对齐。但是，全局特征上的图像级对齐可能会同时纠缠前景/背景像素，而实例级别对齐使用建议可能会遭受背景噪声的影响。与现有解决方案不同，我们提出了一个域的适应框架，该框架通过预测像素的对象和中心度来解释每个像素。具体而言，所提出的方法通过更多地关注前景像素来实现中心意识的一致性，从而实现跨领域的更好适应。我们在众多适应设置上演示了我们的方法，并具有广泛的实验结果，并显示出对现有最新算法的有利性能。
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.
