liz6688 Goto Github PK
Type: User
Company: Beijing Jiaotong University
Location: Haidian, Beijing
Type: User
Company: Beijing Jiaotong University
Location: Haidian, Beijing
Multi-label Image Recognition by Recurrently Discovering Attentional Regions (Pytorch implementation)
AttGAN Tensorflow Arbitrary Facial Attribute Editing: Only Change What You Want
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning
CLM-framework (a.k.a Cambridge Face Tracker) is a framework for various Constrained Local Model based face tracking and landmark detection algorithms and their extensions/applications. Includes CLM-Z and CLNF.
Javascript library for precise tracking of facial features via Constrained Local Models
Face Alignment using Convolutional Neural Networks
Predict facial landmarks with Deep CNNs powered by Caffe.
Practise of DeepID for Face Classification
code for DeepProposal paper presented in ICCV 2015
Code and pertained models for the paper "Distilling Localization for Self-Supervised Representation Learning"
Unofficial Tensorflow Implementation of Dual-attention Guided Dropblock Module https://arxiv.org/abs/2003.04719
Face alignment in 3000 FPS
Face landmarks(fiducial points) detection benchmark
[BMVC 2021]: This is the github repository for "FS-QAT : Few Shot Temporal Action Localization using Query Adaptive Transformers" accepted in British Machine Vision Conference (BMVC) 2021, Virtual
[CVPR 2022] PyTorch implementation of Hierarchical Contrastive Selective Coding (HCSC) (https://arxiv.org/abs/2202.00455)
PyTorch implementation of the InfoNCE loss for self-supervised learning.
[ECCV'20 Spotlight] Memory-augmented Dense Predictive Coding for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.
Pytorch implementation of Multimodal Unsupervised Image-to-Image Translation
A MXNet implementation of Mask R-CNN
MTCNN face detection
Object-aware Contrastive Learning for Debiased Scene Representation (NeurIPS 2021)
Object Detection in images using Selective Search and EdgeBoxes algorithm
PyTorch implementation of PixelCNN from "Pixel Recurrent Neural Networks"
Python implementation of the PAMI 2012 paper "Measuring the Objectness of Image Windows" and the CVPR 2010 paper "What is an object ?"
label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
pytorch replicate of TP-GAN "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.