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Type: Organization
Type: Organization
Pytorch implementation of "AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders"
A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
Awesome List of Masked Image Modeling (MIM) Papers for Self-supervised Visual Representation Learning
ECCV2022,Bootstrapped Masked Autoencoders for Vision BERT Pretraining
CatMAE
[AAAI 2023] CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
[AAAI 2023] Contrastive Masked Autoencoders for Self-Supervised Video Hashing
Official codes for ConMIM (ICLR 2023)
ConvMAE: Masked Convolution Meets Masked Autoencoders
Official Implementation of the CrossMAE paper: Rethinking Patch Dependence for Masked Autoencoders
Cross-Sensor Masked Autoencoder for Content Based Image Retrieval in Remote Sensing
Denoising Masked Autoencoders Are Certifiable Robust Vision Learners.
PyTorch implementation of Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
FastMIM, official pytorch implementation of our paper "FastMIM: Expediting Masked Image Modeling Pre-training for Vision"(https://arxiv.org/pdf/2212.06593.pdf).
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
[CVPR'23] Hard Patches Mining for Masked Image Modeling
i-mae Pytorch Repo
[CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
[ICLR 2023] “ Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Better Representations”, Ziyu Jiang, Yinpeng Chen, Mengchen Liu, Dongdong Chen, Xiyang Dai, Lu Yuan, Zicheng Liu, Zhangyang Wang
[CVPR 2023 Highlight(Oral)] Masked Image Modeling with Local Multi-Scale Reconstruction
[MICCAI-2022] This is the official implementation of Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training.
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
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