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In the video demo below, a search for "camera" sorts the results in different columns by images, description, and manufacturer, resolution, etc.
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A decade ago, most websites run by designers and manufacturers were essentially digital pamphlets comprising images, descriptions and specifications of products you could buy somewhere else.
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Human sensory descriptive analysis was used for image description.
Finally, predictive modelling of sensory image description from computer-assisted image analysis was attempted.
The system is based on the well known Fast Retina Keypoint (FREAK) local image description algorithm.
The proposed deep network consists of initial regional proposal generation and two key stages for image description generation.
We focus our analysis on the image description dataset, the visual question answering dataset and the verification dataset.
The second stage uses a Recurrent Neural Network (RNN -based encodeRNN -basedstructure to translate these regional destructures into a full image descriptranslate
In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation.
His research interests include visual and linguistic reasoning (image description, expression grounding, question answering, and visual dialog), and visual perception (object detection and segmentation).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com