Popular GitHub Repositories for Pixel Objectness

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.

Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

Turn any face into a video game character, pixel art, claymation, 3D or toy

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

This project extends the idea of the innovative architecture of Kolmogorov-Arnold Networks (KAN) to the Convolutional Layers, changing the classic linear transformation of the convolution to learnable non linear activations in each pixel.

AI-powered Text-to-Art Generator - Text2Art.com

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"

Pixel-Adaptive Convolutional Neural Networks (CVPR '19)

in progress

Tensorflow implementation of pixel-recursive-super-resolution(Google Brain paper: https://arxiv.org/abs/1702.00783)

Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network

DrQ: Data regularized Q

Pixel-wise segmentation on VOC2012 dataset using pytorch.

Fix AI pixel art and vector images right in your browser

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021

Research code for pixel-based encoders of language (PIXEL)

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.