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Datasets

Greatest papers with code

Focal Loss for Dense Object Detection

ICCV 2017 tensorflow/models

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 REAL-TIME OBJECT DETECTION REGION PROPOSAL

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

NeurIPS 2015 facebookresearch/detectron

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

NeurIPS 2019 open-mmlab/mmdetection

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

CVPR 2019 open-mmlab/mmdetection

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

5 Jun 2019fizyr/keras-retinanet

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 SKIN LESION IDENTIFICATION

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

CVPR 2018 charlesq34/pointnet

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 FEATURE ENGINEERING OBJECT LOCALIZATION REGION PROPOSAL

SNIPER: Efficient Multi-Scale Training

NeurIPS 2018 MahyarNajibi/SNIPER

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