LoFTR Code for “LoFTR: Detector-Free Local Feature Matching with Transformers”, CVPR 2021 LoFTR: Detector-Free Local Feature Matching with TransformersJiaming Sun*, Zehong Shen*, Yu’ang Wang*, Hujun Bao, Xiaowei ZhouCVPR 2021 TODO List and ETA The entire codebase for data pre-processing, training and validation is under major refactoring and will be released around June.Please subscribe to this discussion thread if you wish to be notified of the code release.In the meanwhile, discussions about the paper are welcomed in the discussion panel. [x] […]

PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback LoopHongwen Zhang*, Yating Tian*, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, Zhenan Sun Requirements packages necessary files mesh_downsampling.npz & DensePose UV data Run the following script to fetch mesh_downsampling.npz & DensePose UV data from other repositories. bash fetch_data.sh SMPL model files Fetch preprocessed data from SPIN. Download the pre-trained model and put it into the ./data/pretrained_model directory. After […]

AOT-GAN-for-Inpainting AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. AOT-GAN: Aggregated Contextual Transformations for High-Resolution Image InpaintingYanhong Zeng, Jianlong Fu, Hongyang Chao, and Baining Guo. Citation If any part of our paper and code is helpful to your work,please generously cite and star us :kissing_heart: :kissing_heart: :kissing_heart: ! @inproceedings{yan2021agg, author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining}, title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting}, booktitle […]

ACSL This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection. Requirements 1. Environment: The requirements are exactly the same as BalancedGroupSoftmax. We tested on the following settings: python 3.7 cuda 10.0 pytorch 1.2.0 torchvision 0.4.0 mmcv 0.2.14 conda create -n mmdet python=3.7 -y conda activate mmdet pip install cython pip install numpy pip install torch pip install torchvision pip install pycocotools pip install matplotlib pip install terminaltables # download the source […]

carefree-learn carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch. Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side import cflearn import numpy as np x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make().fit(x, y) Developer Side import cflearn import numpy as np cflearn.register_model(“wnd_full”, pipes=[cflearn.PipeInfo(“fcnn”), cflearn.PipeInfo(“linear”)]) x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make(“wnd_full”).fit(x, y) Please refer to Quick Start and Build Your Own […]