#计算机科学#OpenMMLab Pre-training Toolbox and Benchmark
翻译 - OpenMMLab图像分类工具箱和基准
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
翻译 - 自我监督学习工具箱和基准
#计算机科学#[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling...
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".
翻译 - 这是“SimMIM: A Simple Framework for Masked Image Modeling”的官方实现。
#Awesome#CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
ConvMAE: Masked Convolution Meets Masked Autoencoders
[ICCV 2023] You Only Look at One Partial Sequence
#Awesome#[Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)
Official Codes for "Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality"
This is a PyTorch implementation of “Context AutoEncoder for Self-Supervised Representation Learning"
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning
Official PyTorch implementation of MOOD series: (1) MOODv1: Rethinking Out-of-distributionDetection: Masked Image Modeling Is All You Need. (2) MOODv2: Masked Image Modeling for Out-of-Distribution...
PyTorch code for MUST
This is a PyTorch implementation of “Context AutoEncoder for Self-Supervised Representation Learning"
[CVPR'23] Hard Patches Mining for Masked Image Modeling
A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners
[ICLR2024] Exploring Target Representations for Masked Autoencoders
[NIPS'23] Official Code of the paper "Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing"
PyTorch reimplementation of "A simple, efficient and scalable contrastive masked autoencoder for learning visual representations".