论文标题
区域建议本地化与域自适应对象检测分类之间的协作培训
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
论文作者
论文摘要
对象探测器通常经过大量标记的数据训练，该数据既昂贵又富含劳动力。应用于未标记数据集的预训练检测器总是遭受数据集分布的差异，也称为域移位。对象检测的域适应性试图使检测器从标记的数据集适应未标记的数据集以提高性能。在本文中，我们是第一个揭示区域提案网络（RPN）和区域提案分类器〜（RPC）中的区域探测器（例如，更快的RCNN）在面对大域间隙时表现出显着差异的可传递性。区域分类器表现出优选的性能，但没有RPN的高质量建议，而骨干网络中的简单对齐不足以适应RPN。我们深入研究了RPN和RPC的一致性和差异，单独对待它们，并利用一个人作为相互指导的高信心输出来训练另一个。此外，使用低信心的样品用于RPN和RPC和Minimax优化之间的差异计算。各种情况上的广泛实验结果证明了我们提出的方法在域自适应区域提案生成和对象检测中的有效性。代码可从https://github.com/ganlongzhao/cst_da_detection获得。
Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer from the difference of dataset distribution, also called domain shift. Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier~(RPC) in the endemic two-stage detectors (e.g., Faster RCNN) demonstrate significantly different transferability when facing large domain gap. The region classifier shows preferable performance but is limited without RPN's high-quality proposals while simple alignment in the backbone network is not effective enough for RPN adaptation. We delve into the consistency and the difference of RPN and RPC, treat them individually and leverage high-confidence output of one as mutual guidance to train the other. Moreover, the samples with low-confidence are used for discrepancy calculation between RPN and RPC and minimax optimization. Extensive experimental results on various scenarios have demonstrated the effectiveness of our proposed method in both domain-adaptive region proposal generation and object detection. Code is available at https://github.com/GanlongZhao/CST_DA_detection.
