Region Proposal

92 papers with code • 1 benchmarks • 5 datasets

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Greatest papers with code

Focal Loss for Dense Object Detection

tensorflow/models 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.

Dense Object Detection Long-tail Learning +2

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

facebookresearch/detectron 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.

Real-Time Object Detection Region Proposal

Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

open-mmlab/mmdetection NeurIPS 2019

This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.

Object Detection Region Proposal

Region Proposal by Guided Anchoring

open-mmlab/mmdetection CVPR 2019

State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.

Object Detection Region Proposal

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

fizyr/keras-retinanet 5 Jun 2019

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

Computed Tomography (CT) Region Proposal +1

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

charlesq34/pointnet 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.

3D Object Detection Autonomous Navigation +4

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.

Object Detection Region Proposal