This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer.Haoyu Chen, Hao Tang, Nicu Sebe, Guoying Zhao. Citation If you use our code or paper, please consider citing: @inproceedings{chen2021AniFormer, title={AniFormer: Data-driven 3D Animation withTransformer}, author={Chen, Haoyu and Tang, Hao and Sebe, Nicu and Zhao, Guoying}, booktitle={BMVC}, year={2021} } Dependencies Requirements: python3.6 numpy pytorch==1.1.0 and above trimesh Dataset preparation Please download DFAUST dataset    

This repository is the official implementation of CARE paper. Updates (09/10/2021) Our paper is accepted by NeurIPS 2021. (14/10/2021) Our code is available for ssl pretraining and image classification evaluation. Comming Provide resnet_50(100e) models and logs. Complete the pretrained models and logs. Requirements To install requirements: conda create -n care python=3.6 conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1    

This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.We learn local State-Action-Reward representations (StAR-representations) to improve (long) sequence modeling for reinforcement learning (and imitation learning). Results Installation Dependencies can be installed by Conda: conda env create -f my_env.yml And install Atari ROMs. Datasets Please follow this instruction for datasets. Example usage See run.sh or below:

optional arguments: -h, –help show this help message and exit –gpu_device GPU_DEVICE Select specific GPU to run the model –batch-size N Input batch size for training (default: 64) –epochs N Number of epochs to train (default: 20) –num-class N Number of classes to classify (default: 10) –lr LR Learning rate (default: 0.01) –weight-decay WD Weight decay (default: 1e-5) –model-path PATH Path to save    

This repository contains the code in both PyTorch and TensorFlow for our paper Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov (*: equal contribution) Preprint 2018 TensorFlow The source code is in the tf/ folder, supporting (1) single-node multi-gpu training, and (2) multi-host TPU training. Besides the source code, we also provide pretrained “TensorFlow” models with state-of-the-art (SoTA) performances reported in the paper. Please refer to tf/README.md […]

By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, Hongsheng Li. This repo is the official Pytorch implementation of FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. Introduction Usage Prerequisites Install git clone https://github.com/ruiliu-ai/FuseFormer.git cd FuseFormer pip install -r requirements.txt Training Dataset preparation Download datasets (YouTube-VOS and DAVIS) into the data folder. mkdir data Training script python train.py -c configs/youtube-vos.json Test Download pre-trained model into checkpoints folder. mkdir checkpoints Test script […]

Code for paper “nnFormer: Interleaved Transformer for Volumetric Segmentation “. Please read our preprint at the following link: paper_address. Parts of codes are borrowed from nn-UNet. Installation 1、System requirements This software was originally designed and run on a system running Ubuntu 18.01, with Python 3.6, PyTorch 1.8.1, and CUDA 10.1. For a full list of software packages and version numbers, see the Conda environment file environment.yml. This software leverages graphical processing units (GPUs) to accelerate neural network training and evaluation; […]

This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Window Transformer (arxiv, supp). SwinIR ahcieves state-of-the-art performance in bicubic/lighweight/real-world image SR grayscale/color image denoising JPEG compression artifact reduction 🚀 🚀 🚀News: Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance […]

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of the input sequence, using the scheme from the PonderNet paper. Will also try to abstract out a pondering module that can be used with any block that returns an output with the halting probability. This repository would not have been possible without repeated viewings of Yannic’s educational video Install $ pip install ponder-transformer Usage import torch from ponder_transformer […]

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 […]