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

• URL: http://arxiv.org/abs/2007.13816v1
• Date: Mon, 27 Jul 2020 19:04:57 GMT
• Title: Corner Proposal Network for Anchor-free, Two-stage Object Detection
• Authors: Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi Tian
• Abstract summary: 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. We demonstrate that these two stages are effective solutions for improving recall and precision.
• Score: 174.59360147041673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection [26.843891792018447]
  We present PETDet (Proposal Enhancement for Two-stage fine-grained object detection) to better handle the sub-tasks in two-stage FGOD methods. An anchor-free Quality Oriented Proposal Network (QOPN) is proposed with dynamic label assignment and attention-based decomposition. A novel Adaptive Recognition Loss (ARL) offers guidance for the R-CNN head to focus on high-quality proposals.
  arXiv  Detail & Related papers  (2023-12-16T18:04:56Z)
• Point-to-Box Network for Accurate Object Detection via Single Point Supervision [51.95993495703855]
  We introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method. P2BNet can construct an inter-objects balanced proposal bag by generating proposals in an anchor-like way. The code will be released at COCO.com/ucas-vg/P2BNet.
  arXiv  Detail & Related papers  (2022-07-14T11:32:00Z)
• CenterNet++ for Object Detection [174.59360147041673]
  Bottom-up approaches are as competitive as the top-down and enjoy higher recall. Our approach, named CenterNet, detects each object as a triplet keypoints (top-left and bottom-right corners and the center keypoint) On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieves APs of 53.7% and 57.1%, respectively.
  arXiv  Detail & Related papers  (2022-04-18T16:45:53Z)
• EPP-Net: Extreme-Point-Prediction-Based Object Detection [9.270523894683278]
  We present a new anchor-free dense object detector, which regresses the relative displacement vector between each pixel and the four extreme points. We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU) On the MS-COCO dataset, our method achieves an average precision (AP) of 39.3% with ResNet-50 and an AP of 48.3% with ResNeXt-101-DCN.
  arXiv  Detail & Related papers  (2021-04-29T01:01:50Z)
• PDNet: Towards Better One-stage Object Detection with Prediction Decoupling [37.83405509385431]
  We propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm. With a single ResNeXt-64x4d-101 as the backbone, our detector achieves 48.7 AP with single-scale testing.
  arXiv  Detail & Related papers  (2021-04-28T16:48:04Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• FCOS: A simple and strong anchor-free object detector [111.87691210818194]
  We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free.
  arXiv  Detail & Related papers  (2020-06-14T01:03:39Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.