论文标题
自适应对象检测的渠道对齐
Channel-wise Alignment for Adaptive Object Detection
论文作者
论文摘要
在过去的十年中,通过深度卷积神经网络的发展,通用对象检测得到了极大的促进。但是,在域转移情况下,天气,照明等的变化通常会导致域间隙,因此当从一个域到另一个域检测对象时,性能会大大下降。此任务上的现有方法通常会根据整个图像或感兴趣的对象引起人们对高级对齐的关注,而这些图像自然无法完全利用细颗粒的通道信息。在本文中,我们从截然不同的角度(即渠道对齐方式)实现了适应。由于每个通道都集中在特定模式(例如,在特殊的语义区域(例如汽车)上)的发现,我们旨在使源和目标域在通道级别上的分布保持一致,这对于在差异域之间的集成更为优先。我们的方法主要由自通道和跨通道对齐。这两个部分从通道的角度隐含了注意区域的内部关系和交叉关系。此外,我们还提出了一个RPN域分类器模块,以获得域不变的RPN网络。广泛的实验表明,所提出的方法的性能要比现有方法高,在各种域换档设置下提高了约5%。关于不同任务的实验(例如实例分割)也证明了其良好的可扩展性。
Generic object detection has been immensely promoted by the development of deep convolutional neural networks in the past decade. However, in the domain shift circumstance, the changes in weather, illumination, etc., often cause domain gap, and thus performance drops substantially when detecting objects from one domain to another. Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information. In this paper, we realize adaptation from a thoroughly different perspective, i.e., channel-wise alignment. Motivated by the finding that each channel focuses on a specific pattern (e.g., on special semantic regions, such as car), we aim to align the distribution of source and target domain on the channel level, which is finer for integration between discrepant domains. Our method mainly consists of self channel-wise and cross channel-wise alignment. These two parts explore the inner-relation and cross-relation of attention regions implicitly from the view of channels. Further more, we also propose a RPN domain classifier module to obtain a domain-invariant RPN network. Extensive experiments show that the proposed method performs notably better than existing methods with about 5% improvement under various domain-shift settings. Experiments on different task (e.g. instance segmentation) also demonstrate its good scalability.