DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
This work addresses object detection for computer vision applications, representing an incremental improvement through novel architectural components.
The paper tackles improving object detection by introducing a backbone design with Recursive Feature Pyramid and Switchable Atrous Convolution, resulting in DetectoRS achieving state-of-the-art performances of 55.7% box AP on COCO test-dev.
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.