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Image Captioning

by py_team3
December 9, 2020
in Courses, Python/DL Programming
2 min read
115
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Story Telling Data Sharing ML Experience Applications Ethics Presentation

Reference: http://ocel.ai/story-telling/

  • Who are the people or communities in need of help? 
    • Our goal for this project is to build a tool that allows a computer to be able to recognize the contents of an image and correctly identify the relationships between them. While this tool would be valuable across a swath of sectors, it would be of particular value to those who are visually impaired. This image captioning tool could easily evolve beyond describing simple stationary images and could potentially perform complicated tasks such as describing images in emails and on social media, describing scenes in movies, and identifying household items – tasks that would allow the visually impaired to better navigate and participate in the world.
  • What problem happened to them? 
    • Visual impairment significantly reduces a person’s ability to participate in virtual spaces, such as social media platforms, and their ability to use a majority of digital tools, such as phone apps.
  • When did the problem take place?
    • The general problem of visual impairment is ongoing. According to the World Health Organization, 285 million people are estimated to be visually impaired, 39 million of whom are blind.
  • Where means two things: 1) The environment and settings that the people or the community is living in, and 2) the place/location where the problem take place.
    • As stated previously, the problem of accessibility for visually impaired people is global. Our took can help increase accessibility wherever digital tools are used, such as the workplace, schools, or homes.  
  • Why means the possible causes and/or origin of the problem. 
    • Because so many digital tools and platforms are visual in nature, they are often inaccessible to people with visual impairments. By creating a tool that allows a computer to caption images, we can circumvent this problem and make the use of digital tools less dependent on the ability to see.


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This work was partially sponsored by NSF.

NSF IUSE #1935076
CUE Ethics: Collaborative Research: Open Collaborative Experiential Learning (OCEL.AI): Bridging Digital Divides in Undergraduate Education of Data Science

01/01/2020 – 6/30/2021, $ 350,000

Copyright © 2020 OCEL.AI.

 

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