论文标题
探测器：检测具有递归特征金字塔和可切换的卷积的对象
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
论文作者
论文摘要
许多现代对象探测器通过使用两次思考和思考的机制表现出出色的性能。在本文中，我们在骨干设计中探索了该机制以进行对象检测。在宏观层面，我们提出了递归特征金字塔，该金字塔将特征金字塔网络中的额外反馈连接到自下而上的主链层。在微观级别，我们提出了可开关的抗衡卷积，该卷积会以不同的耐心速率进行卷积，并使用开关功能收集结果。将它们组合在一起会导致检测器，从而显着改善了对象检测的性能。在可可测试-DEV上，检测器可实现最新的55.7％盒子AP用于对象检测，48.5％的掩码AP，例如分段，用于50.0％的PQ用于PANOPTIC分段。该代码可公开可用。
Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available.
