Search Results
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
facebookresearch/detectron
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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.
Ranked #2 on
Vessel Detection
on Vessel detection Dateset
Fast R-CNN
facebookresearch/detectron
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ICCV 2015
Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks.
Ranked #23 on
Object Detection
on PASCAL VOC 2007
(using extra training data)
Non-local Neural Networks
facebookresearch/detectron
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CVPR 2018
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
Ranked #8 on
Action Classification
on Toyota Smarthome dataset
(using extra training data)
Learning to Segment Every Thing
facebookresearch/detectron
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CVPR 2018
Most methods for object instance segmentation require all training examples to be labeled with segmentation masks.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
facebookresearch/detectron
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NeurIPS 2016
In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
Ranked #4 on
Real-Time Object Detection
on PASCAL VOC 2007
Feature Pyramid Networks for Object Detection
facebookresearch/detectron
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CVPR 2017
Feature pyramids are a basic component in recognition systems for detecting objects at different scales.
Ranked #3 on
Pedestrian Detection
on TJU-Ped-campus
Detecting and Recognizing Human-Object Interactions
facebookresearch/detectron
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CVPR 2018
Our hypothesis is that the appearance of a person -- their pose, clothing, action -- is a powerful cue for localizing the objects they are interacting with.
Ranked #53 on
Human-Object Interaction Detection
on HICO-DET
Mask R-CNN
facebookresearch/detectron
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on
Keypoint Estimation
on GRIT
DensePose: Dense Human Pose Estimation In The Wild
facebookresearch/detectron
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CVPR 2018
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
Ranked #2 on
Pose Estimation
on DensePose-COCO
Data Distillation: Towards Omni-Supervised Learning
facebookresearch/detectron
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CVPR 2018
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.
