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CAPTION GENERATION OF IMAGES USING CNN AND LSTM

Authors: Ummar Yousuf; Ravinder Pal Singh; Monika Mehra;

CAPTION GENERATION OF IMAGES USING CNN AND LSTM

Abstract

The contents of a picture are automatically created in Artificial Intelligence (AI), which combines computer vision and natural language processing (NLP) (Natural Language Processing). It is developed a regenerative neuronal model. Computer vision and machine translation are required. This model is used to produce natural-sounding phrases that describe the picture. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are used in this model (RNN). The CNN is used to extract features from images, while the RNN is used to generate sentences. The model has been trained in such a manner that when an input image is provided to it, it creates captions that almost accurately describe the image. On various datasets, the model's accuracy, smoothness, and command of language learned from picture descriptions are assessed. These tests reveal that the model typically provides correct descriptions of the input image.

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Keywords

Long Short Term Memory (LSTM), Deep Learning, Neural Network, Image, Caption, Description

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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