CaoZhonglei

CaoZhonglei's Stars

• xmu-xiaoma666/External-Attention-pytorch

  🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

  Language:Python11.7k103831.9k

• JiahuiYu/generative_inpainting

  DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

  Language:Python3.3k74534788

• philipperemy/keras-attention

  Keras Attention Layer (Luong and Bahdanau scores).

  Language:Python2.8k7856673

• LeeJunHyun/Image_Segmentation

  Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

  Language:Python2.8k2593604

• MenghaoGuo/Awesome-Vision-Attentions

  Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

  Language:Python2.8k3211411

• junfu1115/DANet

  Dual Attention Network for Scene Segmentation (CVPR2019)

  Language:Python2.4k36144482

• bojone/attention

  some attention implements

  Language:Python1.4k3221512

• YimianDai/open-aff

  code and trained models for "Attentional Feature Fusion"

  Language:Python75884295

• datalogue/keras-attention

  Visualizing RNNs using the attention mechanism

  Language:Python7502649242

• backseason/PoolNet

  Code for our CVPR 2019 paper "A Simple Pooling-Based Design for Real-Time Salient Object Detection"

  Language:Python6291887152

• taki0112/Self-Attention-GAN-Tensorflow

  Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)

  Language:Python5521527148

• PSMM/SLIC-Superpixels

  Implementation of the SLIC superpixel algorithm to work with OpenCV

  Language:C++292185147

• lim-anggun/FgSegNet

  FgSegNet: Foreground Segmentation Network, Foreground Segmentation Using Convolutional Neural Networks for Multiscale Feature Encoding

  Language:Jupyter Notebook243142976

• Minerva-J/Pytorch-Segmentation-multi-models

  Pytorch implementation for Semantic Segmentation with multi models (Deeplabv3, Deeplabv3_plus, PSPNet, UNet, UNet_AutoEncoder, UNet_nested, R2AttUNet, AttentionUNet, RecurrentUNet,, SEGNet, CENet, DsenseASPP, RefineNet, RDFNet)

  Language:Python1913743

• lim-anggun/FgSegNet_v2

  FgSegNet_v2: "Learning Multi-scale Features for Foreground Segmentation.” by Long Ang LIM and Hacer YALIM KELES

  Language:Python153112144

• plstcharles/litiv

  C++ implementation pool for computer vision R&D projects.

  Language:C++102131531

• np-csu/SLIC-superpixel-with-OpenCV

  Implementation of the SLIC superpixel algorithm to work with OpenCV2

  Language:C++688126

• ml-postech/adaptive-superpixel-for-active-learning-in-semantic-segmentation

  Language:Python16801

• CIVA-Lab/Motion-U-Net

  Motion U-Net is multi-cue autoencoder deep architecture for robust moving object detection

  Language:Python12533

• Billccx/SLIC

  Superpixels Compared to State-of-the-Art Superpixel Methods

  Language:C++100

• safaabbes/Motion-Detection-Techniques-using-OpenCV

  Basic Motion Detection Techniques using OpenCV (Frame Differencing, Background Substraction, MoG...)

  Language:Python73

• anilturker/fgsegnet_v2_pytorch

  FgSegNet : Foreground Segmentation Network

  Language:Python42

• deropty/BoBS

  Block-based background substraction for surveillance video

  Language:C++2101

• jeanfang/subsense-GMM

  subsense&GMM in one project

  Language:C++2000

• ahester57/slic_superpixels

  (C++) OpenCV SLIC Superpixel Segmentation

  Language:C++1300

• Anjaneyakarjigi/AREA_MONITORING

  Restricted area monitoring based on contours detection and image background substraction.

  Language:C++1

• ercanserteli/video_segmentation

  Video segmentation with SLIC Superpixels and SVM classification

  Language:C++1100

• fderue/Slic

  Superpixel SLIC for CPU

  Language:C++11

• gmrukwa/slico

  Implementation of SLICO algorithm for superpixel estimation

  Language:C++1200

• kuni23/Background-segmentation-implemented-on-video-sequences

  Background segmentation is implemented in C++ language. You should be able to use the provided codes by setting the paths of the videos you want to use. Shadow detection and more advanced background substraction models are also implemented such as the GMM.

  Language:C++1