Deep Learning

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

ActionDetection-DBG In this repo, we propose a novel and unified action detection framework, named DBG, with superior performance over the state-of-the-art action detectors BSN and BMN. You can use the code to evaluate our DBG for action proposal generation or action detection. For more details, please refer to our paper Fast Learning of Temporal Action Proposal via Dense Boundary Generator! Paper Introduction This paper introduces a novel and unified temporal action proposal generator named Dense Boundary Generator (DBG). In this […]

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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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