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residual-attention-network

Residual Attention Network for Image Classification

residualattentionnetwork-pytorch

a pytorch code about Residual Attention Network. This code is based on two projects from

resnext-densenet

PyTorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt, DenseNet, and Group Normalisation

rethinking-network-pruning

Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

retinexnet

A Tensorflow implementation of RetinexNet

reviewercertificates

pdfs

revnet-public

Code for "The Reversible Residual Network: Backpropagation Without Storing Activations"

rfbnet

Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

rgbdslam_v2

RGB-D SLAM for ROS

riciannet

MRI denoising used RicianNet

riskcontrol

rmf

Robust Matrix Factorization by Majorization Minimization

robustpca

Robust PCA implementation and examples (Matlab)

rpcaadmm

Robust Principal Component Analysis via ADMM in Python

rssc

rssc-cviu2015

Re-weighted Sparse Subspace Clustering, Computer Vision and Image Understanding, 2015.

sa-ssd

sact

Spatially Adaptive Computation Time for Residual Networks

sae

Semantic Autoencoder for Zero-shot Learning (Spotlight), CVPR 2017

saist_modified

MATLAB CODE

sapien-release

sca-cnn.cvpr17

Image Captions Generation with Spatial and Channel-wise Attention

scatnet

Scattering Network for Matlab

schatten-p_codes

code of top published work

scielab-1996

Initial S-CIELAB Implementation (Zhang and Wandell)

scikit-learn

scikit-learn: machine learning in Python

sds

Code for SDS

seeing-motion-in-the-dark

Seeing Motion in the Dark. ICCV19

segmentation

In this project, various image segmentation method on medical image is provided and tested.

segmentation-of-ultrasound-images

The purpose of this study is the contour extraction of a region of interest in ultrasonic images. I applied the anisotropic diffusion algorithm to preprocess images, then an active contour model using a gradient vector flow was employed. In the end, the contour of a lesion area of the ultrasonic images was extracted.