Region Proposal

112 papers with code • 1 benchmarks • 5 datasets

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Libraries

Use these libraries to find Region Proposal models and implementations
4 papers
20,897
2 papers
12,231
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Most implemented papers

Focal Loss for Dense Object Detection

facebookresearch/detectron ICCV 2017

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

rbgirshick/py-faster-rcnn NeurIPS 2015

In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.

Frustum PointNets for 3D Object Detection from RGB-D Data

charlesq34/frustum-pointnets CVPR 2018

In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

qianguih/voxelnet CVPR 2018

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

maudzung/Complex-YOLOv4-Pytorch 16 Mar 2018

We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.

Domain Adaptive Faster R-CNN for Object Detection in the Wild

yuhuayc/da-faster-rcnn CVPR 2018

The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.

Arbitrary-Oriented Scene Text Detection via Rotation Proposals

mjq11302010044/RRPN 3 Mar 2017

This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images.

Joint 3D Proposal Generation and Object Detection from View Aggregation

kujason/avod 6 Dec 2017

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.

SNIPER: Efficient Multi-Scale Training

MahyarNajibi/SNIPER NeurIPS 2018

Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47. 6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU.