Masked autoencoder (MAE) is a self-supervised learning method that outperforms supervised learning methods without relying on large-scale databases. The MAE can improve the feature extraction ability of the model effectively. The multi-scale convolution strategy can expand the receptive field and ...
Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular point clouds. In this paper, we propose Point...
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks论文阅读中的问题和思考 1.有监督的串行GAN网络结构 在图示的网络结果中,G1的输入是真实图像编码后的结果,而非直接输入真实图像。这个应该是VAE(Variational Autoencoder, ... ...
boundary guidance, neighbor guidance, and global guidance) to generate more fine-grained results through a step-by-step refinement process. Extensive experiments on three benchmark datasets demonstrate that our method significantly outperforms 30 competing approaches. Our code is available at...
Masked autoencoders for point cloud self-supervised learning. In ECCV, 2022. 8 [68] Keunhong Park, Konstantinos Rematas, Ali Farhadi, and Steven M Seitz. Photoshape: photorealistic materials for large-scale shape collections. ACM Trans. Graph., 2018. 3 [69] Federico Perazzi, Jo...
4543Accesses 52Altmetric Metrics Abstract Despite the success of pretrained natural language processing (NLP) models in various fields, their application in computational biology has been hindered by their reliance on biological sequences, which ignores vital three-dimensional (3D) structural information in...
On the other hand, masked autoencoders (MAEs) [27] demonstrate commendable image understanding capabilities through self-supervised learning. The simplified mask image modeling (SimMIM) [28], tailored specifically for the Swin-Transformer, uses a straightforward masked image modeling (MIM) approach, ...
The normalized output is then element-wise multiplied by the original SHC to obtain the masked SHC. This mask amplifies significant spatial features while attenuating noise and interference. It is important to note that residual connections are used between the first and last C-Conv layers to ...
3.2. Masked vision encoder Inspired by the efficient reconstruction capabilities of masked autoencoders[34],[54], our approach utilizes a vision encoder[55]with masked inputs to generate a latent representation of the given data. Unlike standard masked autoencoders[34], which aim to reconstruct ...
The normalized output is then element-wise multiplied by the original SHC to obtain the masked SHC. This mask amplifies significant spatial features while attenuating noise and interference. It is important to note that residual connections are used between the first and last C-Conv layers to ...