Paper Citation: @inproceedings{subedi-krishna-bal-2022-cnn, title = "{CNN}-Transformer based Encoder-Decoder Model for {N}epali Image Captioning", author = "Subedi, Bipesh and Krishna Bal, Bal", ...

Parameters set to be learned: All Decoder parameters, the fully connected layer from the Encoder as well as the ... Overall the model has 12.227.735 learnable parameters (the pre-trained CNN extractor ...

A dataset comprising 55,500 samples and 37 classes were used to train the proposed Convolutional Neural Networkmodel(CNN) to recognize 37 classes for.The model contains three convolutional layers and ...

The model is called CNN Encoder-Decoder LSTM (CNN-ED-LSTM) and uses a hybrid Deep Learning approach. The model utilizes an encoder-decoder framework incorporating a Convolutional Neural Network (CNN) ...

In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show ...

This transformation is important because it allows the model to “understand” the input. Then, the decoder uses the information of the encoder and generates an output, such as a translation of the ...