masked-image-modeling

OpenMMLab Pre-training Toolbox and Benchmark

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

Masked Depth Modeling for Spatial Perception

[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".

CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark

ConvMAE: Masked Convolution Meets Masked Autoencoders

[Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)

[ICCV 2023] You Only Look at One Partial Sequence

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 Detection.

This is a PyTorch implementation of “Context AutoEncoder for Self-Supervised Representation Learning"

[CVPR'23 & TPAMI'25] Hard Patches Mining for Masked Image Modeling & Bootstrap Masked Visual Modeling via Hard Patch Mining

PyTorch code for MUST

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"

This is an official implementation of “A Masked Reverse Knowledge Distillation Method Incorporating Global and Local Information for Image Anomaly Detection” (MRKD) with PyTorch, accepted by knowledge-based systems.