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carnd-detect-lane-lines-and-vehicles

Use segmentation networks to recognize lane lines and vehicles. Infer position and curvature of lane lines relative to self.

compressai

A PyTorch library and evaluation platform for end-to-end compression research

compression

Data compression in TensorFlow

dcnv2

Deformable Convolutional Networks v2 with Pytorch

diffusion-models-pytorch

Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)

digital_video_introduction

A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷

dvc

DVC: An End-to-end Deep Video Compression Framework, CVPR 2019 (Oral)

fast-vid2vid

The code for ECCV22 paper "Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis"

high-fidelity-generative-compression

Pytorch implementation of High-Fidelity Generative Image Compression + Routines for neural image compression

image-compression-and-video-coding

image-compression-cnn

Semantic JPEG image compression using deep convolutional neural network (CNN)

intelligent-transportation-system

CV integrated project. Including MOT to track cars and pedestrians, object detection and image classification to get license plate content, semantic segmentation to get zebra crossing, tradition method to get area of lanes .etc.

mmagic

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.

realbasicvsr

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

semantic-segmentation

I will upload many semantic segmentation models to this repository for you to learn

spatiotemporalentropymodel

Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

video-compression-net

A new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole mo

zooming-slow-mo-cvpr-2020

Fast and Accurate One-Stage Space-Time Video Super-Resolution (accepted in CVPR 2020)