Search Results for author:
Found 9 papers, 5 papers with code
Face Alignment at 3000 FPS via Regressing Local Binary Features
The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.
Ranked #26 on
Object Detection
on PASCAL VOC 2007
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
In this work, we study rectifier neural networks for image classification from two aspects.
Object Detection Networks on Convolutional Feature Maps
We discover that aside from deep feature maps, a deep and convolutional per-region classifier is of particular importance for object detection, whereas latest superior image classification models (such as ResNets and GoogLeNets) do not directly lead to good detection accuracy without using such a per-region classifier.
Global Refinement of Random Forest
The global pruning is developed to reduce the model size as well as the over-fitting risk.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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
Deep Residual Learning for Image Recognition
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Ranked #1 on
Image Classification
on cifar100
Identity Mappings in Deep Residual Networks
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors.
Ranked #17 on
Image Classification
on Kuzushiji-MNIST
Instance-sensitive Fully Convolutional Networks
In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances.
