Kaidi Cao's starred repositories
mmdetection
OpenMMLab Detection Toolbox and Benchmark
style2paints
sketch + style = paints :art: (TOG2018/SIGGRAPH2018ASIA)
higgsfield
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
taichi_mpm
High-performance moving least squares material point method (MLS-MPM) solver. (ACM Transactions on Graphics, SIGGRAPH 2018)
kinetics-i3d
Convolutional neural network model for video classification trained on the Kinetics dataset.
hyperlearn
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
rethinking-network-pruning
Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)
pytorch-pose
A PyTorch toolkit for 2D Human Pose Estimation.
tps_stn_pytorch
PyTorch implementation of Spatial Transformer Network (STN) with Thin Plate Spline (TPS)
kinetics_i3d_pytorch
Inflated i3d network with inception backbone, weights transfered from tensorflow
BSN-boundary-sensitive-network
Codes of our paper: "BSN: Boundary Sensitive Network for Temporal Action Proposal Generation"
GAN-Tutorial
Simple Implementation of many GAN models with PyTorch.
ava-dataset
The AVA dataset densely annotates 80 atomic visual actions in 351k movie clips with actions localized in space and time, resulting in 1.65M action labels with multiple labels per human occurring frequently.
DDPAE-video-prediction
Learning to Decompose and Disentangle Representations for Video Prediction, NIPS 2018
i3d-non-local-pytorch
pytorch for i3d_nonlocal
DecoupleLearning
Implementation codes of the decouple learning framework
epic-kitchens-55-starter-kit-action-recognition
:seedling: Starter kit for working with the EPIC-KITCHENS-55 dataset for action recognition or anticipation
factors-variation
Code of the paper `Disentangling factors of variation in deep representations using adversarial training` by Mathieu, Zaho, Ramesh, Sprechmann and LeCun
SRN-Deblur
Repository for Scale-recurrent Network for Deep Image Deblurring https://arxiv.org/abs/1802.01770