Generate caption on images using CNN Encoder- LSTM Decoder structure. This project is the second project of Udacity's Computer Vision Nanodegree and combines computer vision and machine translation techniques. The project's objective is a generative model based on a deep recurrent architecture to generate natural sentences describing an image.

Feb 18, 2021 · In this article I will try to explain sequence to sequence model which is encoder-decoder. Initially this model was developed for machine translation but later it was useful for many other...

We can perform neural machine translation via the seq2seq architecture, which is in turn based on the encoder-decoder model. The encoder and decoder are LSTM networks; The encoder encodes input sentences while the decoder decodes …

Oct 20, 2020 · The encoder is built with an Embedding layer that converts the words into a vector and a recurrent neural network (RNN) that calculates the hidden state, here we will be using Long...

Jan 3, 2019 · It uses encoder decoder arthitecture, which is widely wised in different tasks in NLP, such as Machines Translation, Question Answering, Image Captioning. The model consists of two major components: Encoder: a RNN network, used …

Jan 15, 2019 · seq2seq model is a general purpose sequence learning and generation model. It uses encoder decoder architecture, which is widely wised in different tasks in NLP, such as Machines Translation, Question Answering, Image Captioning. The model consists of major components: Embedding: using low dimension dense array to represent discrete word token.

Aug 14, 2019 · The list below highlights some interesting applications of the Encoder-Decoder LSTM architecture. Machine Translation, e.g. English to French translation of phrases. Learning to Execute, e.g. calculate the outcome of small programs. Image Captioning, e.g. generating a text description for images.

Sequence-to-Sequence Model for Machine Translation 🌐 | A project by Group 6, CDAC DBDA, implementing an Encoder-Decoder architecture with LSTM layers for text sequence generation. Supports training and inference with token-level embeddings, masking, …

Aug 7, 2019 · In this post, you discovered how to best configure an encoder-decoder recurrent neural network for neural machine translation and other natural language processing tasks. Specifically, you learned: The Google study that investigated each model design decision in the encoder-decoder model to isolate their effects.

Image-centric translation can be used for example to use OCR of the text on a phone camera image as input to an MT system to translate menus or street signs. The standard algorithm for MT is the encoder-decoder network, also called the sequence to sequence network, an architecture that can be implemented with RNNs or with Transformers.