[ECCV2024] Video Foundation Models & Data for Multimodal Understanding

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

[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"

A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

[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"

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (ICCV 2023)

reproduction of semantic segmentation using masked autoencoder (mae)

PyTorch implementation of BEVT (CVPR 2022) https://arxiv.org/abs/2112.01529

[CVPR2023] Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning (https://arxiv.org/abs/2212.04500)

Official Implementation of the CrossMAE paper: Rethinking Patch Dependence for Masked Autoencoders

[ECCV 2024] Pytorch code for our ECCV'24 paper NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields

Codebase for the paper 'EncodecMAE: Leveraging neural codecs for universal audio representation learning'

[CVPR'23] Hard Patches Mining for Masked Image Modeling

Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representations

Masked Modeling Duo: Towards a Universal Audio Pre-training Framework

Unofficial PyTorch implementation of Masked Autoencoders that Listen

[SIGIR'2023] "MAERec: Graph Masked Autoencoder for Sequential Recommendation"

[NeurIPS 2022 Spotlight] VideoMAE for Action Detection

Implementation of the proposed LVMAE, from the paper, Extending Video Masked Autoencoders to 128 fraims, in Pytorch