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Many modern object detectors demonstrate outstanding performances by usingthe mechanism of looking and thinking twice. In this paper, we explore thismechanism in the backbone design for object detection. At the macro level, wepropose Recursive Feature Pyramid, which incorporates extra feedbackconnections from Feature Pyramid Networks into the bottom-up backbone layers.At the micro level, we propose Switchable Atrous Convolution, which convolvesthe features with different atrous rates and gathers the results using switchfunctions. Combining them results in DetectoRS, which significantly improvesthe performances of object detection. On COCO test-dev, DetectoRS achievesstate-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instancesegmentation, and 50.0% PQ for panoptic segmentation. The code is made publiclyavailable.

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution