论文标题
跨域对象检测的双维特征对齐
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
论文作者
论文摘要
最近，跨域对象检测的问题已经开始引起计算机视觉社区的注意。在本文中，我们提出了一个新型的无监督的跨域检测模型，该模型利用源域中的带注释的数据来训练对象检测器为不同的目标域训练。提出的模型通过在二维（深度维度和空间维度）上执行跨域特征对准来减轻跨域表示差异以进行对象检测。在通道层的深度维度中，它使用渠道间信息来弥合域差异相对于图像样式对齐。在空间层的维度中，它部署了空间注意模块，以增强检测相关区域并抑制相对于跨域特征比对的无关区域。实验是在许多基准跨域检测数据集上进行的。经验结果表明，所提出的方法的表现优于最先进的比较方法。
Recently the problem of cross-domain object detection has started drawing attention in the computer vision community. In this paper, we propose a novel unsupervised cross-domain detection model that exploits the annotated data in a source domain to train an object detector for a different target domain. The proposed model mitigates the cross-domain representation divergence for object detection by performing cross-domain feature alignment in two dimensions, the depth dimension and the spatial dimension. In the depth dimension of channel layers, it uses inter-channel information to bridge the domain divergence with respect to image style alignment. In the dimension of spatial layers, it deploys spatial attention modules to enhance detection relevant regions and suppress irrelevant regions with respect to cross-domain feature alignment. Experiments are conducted on a number of benchmark cross-domain detection datasets. The empirical results show the proposed method outperforms the state-of-the-art comparison methods.
