Machine Learning

Learning a Contact Potential Field to Model the Hand-Object Interaction

This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-Object InteractionLixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, Cewu LuICCV 2021 1. Get our code: $ git clone –recursive https://github.com/lixiny/CPF.git $ cd CPF 2. Set up your new environment: $ conda env create -f environment.yaml $ conda activate cpf 3. Download asset files Down load our [assets.zip] and unzip it as an assets/ folder. Download the MANO […]

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Detection

Progressive Domain Adaptation for Object Detection

DA_detection Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-faster-rcnn and PyTorch-CycleGAN. Paper Progressive Domain Adaptation for Object DetectionHan-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh and Ming-Hsuan YangIEEE Winter Conference on Applications of Computer Vision (WACV), 2020. Please cite our paper if you find it useful for your research. @inproceedings{hsu2020progressivedet, author = {Han-Kai Hsu and Chun-Han Yao and Yi-Hsuan Tsai and Wei-Chih Hung and Hung-Yu […]

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Search

MMNas: Deep Multimodal Neural Architecture Search

MMNas MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering (VQA), visual grounding (VGD), and image-text matching (ITM) tasks. Prerequisites Software and Hardware Requirements You may need a machine with at least 4 GPU (>= 8GB), 50GB memory for VQA and VGD and 150GB for ITM and 50GB free disk space. We strongly recommend to use a SSD drive to guarantee high-speed I/O. You should first install some necessary […]

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OpenCV

Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM ACMM is a multi-scale geometric consistency guided multi-view stereo method for efficient and accurate depth map estimation. If you find this project useful for your research, please cite: @article{Xu2019ACMM, title={Multi-Scale Geometric Consistency Guided Multi-View Stereo}, author={Xu, Qingshan and Tao, Wenbing}, journal={Computer Vision and Pattern Recognition (CVPR)}, year={2019} } Dependencies The code has been tested on Ubuntu 14.04 with GTX Titan X. Usage cmake . make Use script colmap2mvsnet_acm.py to convert COLMAP SfM result to ACMM input Run ./ACMM $data_folder […]

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Transformer

Rethinking Spatial Dimensions of Vision Transformers

Rethinking Spatial Dimensions of Vision Transformers Byeongho Heo, Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Junsuk Choe, Seong Joon Oh | Paper NAVER AI LAB News Mar 30, 2021: Code & paper released Apr 2, 2021: PiT models with pretrained weights are added to timm repo. You can directly use PiT models with timm>=0.4.7. Jul 23, 2021: Accepted to ICCV 2021 as a poster session Abstract Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision […]

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Machine Learning

Adversarial Training Against Location-Optimized Adversarial Patches

Adversarial-Patch-Training Code for the paper: Sukrut Rao, David Stutz, Bernt Schiele. (2020) Adversarial Training Against Location-Optimized Adversarial Patches. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_32 Setup Requirements Python 3.7 or above PyTorch scipy h5py scikit-image scikit-learn Optional requirements To use script to convert data to HDF5 format torchvision Pillow pandas To use Tensorboard logging With the exception of Python and PyTorch, all requirements […]

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Neural Network

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

StemGNN This repository is the official implementation of Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Requirements Recommended version of OS & Python: To install python dependencies, virtualenv is recommended, sudo apt install python3.7-venv to install virtualenv for python3.7. All the python dependencies are verified for pip==20.1.1 and setuptools==41.2.0. Run the following commands to create a venv and install python dependencies: python3.7 -m venv venv source venv/bin/activate pip install –upgrade pip pip install -r requirements.txt Datasets PEMS03,PEMS04,PEMS07,PEMS08,METR-LA,PEMS-BAY,Solar,Electricity,ECG5000,COVID-19 We can […]

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Computer Vision

CvT: Introducing Convolutions to Vision Transformers

convolution-vision-transformers Pytorch implementation of CvT: Introducing Convolutions to Vision Transformers , for official repo please visit here. Usage: img = torch.ones([1, 3, 224, 224]) model = CvT(224, 3, 1000) parameters = filter(lambda p: p.requires_grad, model.parameters()) parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 print(‘Trainable Parameters: %.3fM’ % parameters) out = model(img) print(“Shape of out :”, out.shape) # [B, num_classes] Citation: @misc{wu2021cvt, title={CvT: Introducing Convolutions to Vision Transformers}, author={Haiping Wu and Bin Xiao and Noel Codella and Mengchen Liu and […]

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Deep Learning

Efficient Deep Neural Network Training via Cyclic Precision

CPT Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accepted at ICLR 2021 (Spotlight) [Paper Link]. Overview Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs’ training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs’ precision might have a similar […]

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Deep Learning

Implementation of lightweight GAN proposed in ICLR 2021 in Pytorch

lightweight-gan 512×512 flowers after 12 hours of training, 1 gpu 256×256 flowers after 12 hours of training, 1 gpu Pizza ‘Lightweight’ GAN Implementation of ‘lightweight’ GAN proposed in ICLR 2021, in Pytorch. The main contributions of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary “converge on single gpu with few hours’ training, on 1024 resolution sub-hundred images”. Install $ pip install lightweight-gan Use One command $ lightweight_gan […]

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