Corner Proposal Network for Anchor-free, Two-stage Object Detection

The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2% which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of 41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at

PDF Abstract ECCV 2020 PDF ECCV 2020 Abstract


Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Detection COCO test-dev CPNDet (Hourglass-104, multi-scale) box AP 49.2 # 66
AP50 67.3 # 61
AP75 53.7 # 45
APS 31.0 # 47
APM 51.9 # 50
APL 62.4 # 40
Hardware Burden None # 1
Operations per network pass None # 1