Before 2015 when the first attention model was proposed, machine translation was based on the simple encoder-decoder model, a stack of RNN and LSTM layers. The encoder is used to process the entire ...

Land cover segmentation is an important and challenging task in the field of remote sensing. Even though convolutional neural networks (CNNs) provide great support for semantic segmentation, standard ...

Contribute to DhruvDR4/Image-Captioning-Attention-Model-Encoder-Decoder development by creating an account on GitHub. ... DhruvDR4/Image-Captioning-Attention-Model-Encoder-Decoder. This commit does ...

I have implemented encoder-decoder based seq2seq models with attention. The first aim for this implementation is to work towards computational grounded theory and thematic analysis using NLP and deep ...

Single image dehazing is a challenging problem in computer vision due to it is highly ill-posed. Although recent research has made great progress, the dehazed images produced by existing models still ...

In particular, for the experiment on the CXR dataset, we used U-Net as the Encoder-Decoder that was pre-trained on a large (non-COVID-19) dataset for lung region semantic segmentation. Using a ...

Although encoder-decoder networks with attention have achieved impressive results in many sequence-to-sequence tasks, the mechanisms behind such networks’ generation of appropriate attention matrices ...