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

2020-06-03 · CVPR 2021 1 · Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

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.

📄 PDF Abstract BibTeX arXiv:2006.02334

Code (6)

joe-siyuan-qiao/DetectoRS 공식 구현 pytorch

FenHua/Robust_Logo_Detection pytorch

TeamA2020/Practice pytorch

Vishal-V/tf-models tf

novav/DetectoRS_Colab

open-mmlab/mmdetection pytorch

Tasks

Instance SegmentationObjectobject-detection Object Detection Panoptic Segmentation Segmentation Semantic Segmentation

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