To tackle the above issue, we present a multilevel contrastive graph masked autoencoder (MCGMAE) for unsupervised GSL. Specifically, we first introduce a graph masked autoencoder with the dual feature ...

The below image is a representation of the basic structure of the mask autoencoder. Image source. A similar method is applied in the Masked Autoencoders Are Scalable Vision Learners work. Model and ...

Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level feature ...

Design a patches masked autoencoder by CNN. Contribute to JJLi0427/CNN_Masked_Autoencoder development by creating an account on GitHub. ... Model Structure. The design involves two stages of work. In ...

The repository gives an improved Transformer-ConvNet architecture with Masked AutoEncoder for Cardiac MRI Registration. The core of the Transformer is designed as a Masked AutoEncoder (MAE) and a ...

The input of the mask branch is the concatenation of the shared features and the boundary prediction. The advantage of this design lies in the ability to bound the fine-grained segmentation masks with ...

In recent years, masked autoencoder (MAE) has been used in various fields due to its powerful self-supervised learning ability and has achieved good results in masked data reconstruction tasks.

Keywords: neural segmentation, SEM image, masked autoencoder, image segmentation, self-supervised learning. Citation: Cheng A, Shi J, Wang L and Zhang R (2023) Learning the heterogeneous ...