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

arXiv: https://arxiv.org/abs/2007.13816 · HTML: https://mlsanity.com/paper/2007.13816/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [Boundary Proposal Network for Two-Stage Natural Language Video Localization](https://mlsanity.com/paper/2103.08109/) (2021)
- [FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors](https://mlsanity.com/paper/2303.08319/) (2023)
- [Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection](https://mlsanity.com/paper/2206.13728/) (2022)
- [CenterNet++ for Object Detection](https://mlsanity.com/paper/2204.08394/) (2022)
- [Location-Sensitive Visual Recognition with Cross-IOU Loss](https://mlsanity.com/paper/2104.04899/) (2021)
- [Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images](https://mlsanity.com/paper/2112.06701/) (2021)
- [A Survey and Evaluation of Adversarial Attacks for Object Detection](https://mlsanity.com/paper/2408.01934/) (2024)
- [Panoptic Perception for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.15388/) (2024)
- [Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network](https://mlsanity.com/paper/2408.03564/) (2024)
- [Gaseous Object Detection](https://mlsanity.com/paper/2502.12415/) (2025)
- [GaTector+: A Unified Head-free Framework for Gaze Object and Gaze Following Prediction](https://mlsanity.com/paper/2510.25301/) (2025)
- [RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection](https://mlsanity.com/paper/2102.04582/) (2021)
- [Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression](https://mlsanity.com/paper/2103.11696/) (2021)

## Abstract (arXiv)

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 https://github.com/Duankaiwen/CPNDet