在对图像patch进行采样后,仅保留25%未被mask的图像patch作为输入,通过linear Projection进行编码后,加上positional embedding,然后输入到一系列的Transformer blocks中。相比于Bert中用mask token来代替被mask区域的做法,MAE encoder直接舍弃掉了mask的部分,通过这种方式可以有效的减少预训练过程中需要消耗的计算资源和训练时...
在对图像patch进行采样后,仅保留25%未被mask的图像patch作为输入,通过linear Projection进行编码后,加上positional embedding,然后输入到一系列的Transformer blocks中。相比于Bert中用mask token来代替被mask区域的做法,MAE encoder直接舍弃掉了mask的部分,通过这种方式可以有效的减少预训练过程中需要消耗的计算资源和训练时...
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild. IEEE Trans. Image Process. 2017, 27, 778–790. [Google Scholar] [CrossRef] [PubMed] Isola, P.; Zhu, J.-Y.; Zhou, T.; Efros, A.A. Image-to-image translation with conditional adversarial networks. In Proceedings of the ...
Mainstream deep learning-based fusion methods are mainly divided into image fusion technology based on autoencoders (AE) [19,20]; image fusion technology based on convolution neural networks (CNN); and image fusion technology based on generative adversarial networks (GAN) [21]. AE-based image ...
The architecture of ResUNet is similar to that of the MS mask DCNN because ResUNet also employs the architecture of the autoencoder. However, MIoUs from ResUNet are lower than those from the MS mask DCNN because the architecture of ResUNet is less effective than that of the MS mask DCNN...