kaiJIN's starred repositories
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
digital_video_introduction
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
Mind-Expanding-Books
:books: Find your next book to read!
pytorch-cnn-visualizations
Pytorch implementation of convolutional neural network visualization techniques
awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
PyTorch-StudioGAN
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
pytorchvideo
A deep learning library for video understanding research.
torch-points3d
Pytorch framework for doing deep learning on point clouds.
pcl-learning
🔥PCL（Point Cloud Library）点云库学习记录
CompressAI
A PyTorch library and evaluation platform for end-to-end compression research
bitsandbytes
Library for 8-bit optimizers and quantization routines.
lapa-dataset
A large-scale dataset for face parsing (AAAI2020)
openpoints
OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [PointNeXt](https://arxiv.org/abs/2206.04670)
Face-Hallucination-Benchmark
A collection of state-of-the-art face super-resolution/hallucination methods
Awesome-ECCV2022-Low-Level-Vision
A Collection of Papers and Codes in ECCV2022 about low level vision
BVQA_Benchmark
A resource list and performance benchmark for blind video quality assessment (BVQA) models on user-generated content (UGC) datasets. [IEEE TIP'2021] "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content", Zhengzhong Tu, Yilin Wang, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
h264decoder
h264 decoding module for python based on libav
Sturcture-Coherency-Face-Alignment
[TIP 2021] We propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose.