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Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
Bounding Box Regression with Uncertainty for Accurate Object Detection (CVPR'19)
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach
A curated list of action recognition and related area resources
机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face D…
A PyTorch Implementation of FaceBoxes
PyTorch implementation of PNASNet-5 on ImageNet
ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
Code for CVPR'18 spotlight "Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer"
Deep 3DMM facial expression parameter extraction
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks
An eyeglass face dataset collected and cleaned for face recognition evaluation, CCBR 2018.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.
Perfect implement. Model shared. x0.5 (Top1:60.646) and 1.0x (Top1:69.402).