AimigoTutor - tutoring application using multi-modal capabilities

Video captioning has been an up-and-coming research topic. Thanks to the recent advances in the performance of deep neural networks, especially with transformers, video captioning is seeing a huge potential improvement in accuracy and versatility. Most state-of-the-art video captioning models employ...

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Main Author: Nguyen, Viet Hoang
Other Authors: Hanwang Zhang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175732
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1757322024-05-10T15:40:40Z AimigoTutor - tutoring application using multi-modal capabilities Nguyen, Viet Hoang Hanwang Zhang School of Computer Science and Engineering hanwangzhang@ntu.edu.sg Computer and Information Science Multi-modal Video captioning has been an up-and-coming research topic. Thanks to the recent advances in the performance of deep neural networks, especially with transformers, video captioning is seeing a huge potential improvement in accuracy and versatility. Most state-of-the-art video captioning models employ a multi-modal approach, whereby both the visual information of the video frames and the audio information of the video are used to extract the semantic meaning of the video. This project will explore the capability of multi-modal video captioning in a much-needed context: building a video tutoring application for students, called AimigoTutor. This report will discuss the requirements, design, implementation and evaluation of the application. Bachelor's degree 2024-05-06T01:46:25Z 2024-05-06T01:46:25Z 2024 Final Year Project (FYP) Nguyen, V. H. (2024). AimigoTutor - tutoring application using multi-modal capabilities. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175732 https://hdl.handle.net/10356/175732 en SCSE23-0209 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Multi-modal
spellingShingle Computer and Information Science
Multi-modal
Nguyen, Viet Hoang
AimigoTutor - tutoring application using multi-modal capabilities
description Video captioning has been an up-and-coming research topic. Thanks to the recent advances in the performance of deep neural networks, especially with transformers, video captioning is seeing a huge potential improvement in accuracy and versatility. Most state-of-the-art video captioning models employ a multi-modal approach, whereby both the visual information of the video frames and the audio information of the video are used to extract the semantic meaning of the video. This project will explore the capability of multi-modal video captioning in a much-needed context: building a video tutoring application for students, called AimigoTutor. This report will discuss the requirements, design, implementation and evaluation of the application.
author2 Hanwang Zhang
author_facet Hanwang Zhang
Nguyen, Viet Hoang
format Final Year Project
author Nguyen, Viet Hoang
author_sort Nguyen, Viet Hoang
title AimigoTutor - tutoring application using multi-modal capabilities
title_short AimigoTutor - tutoring application using multi-modal capabilities
title_full AimigoTutor - tutoring application using multi-modal capabilities
title_fullStr AimigoTutor - tutoring application using multi-modal capabilities
title_full_unstemmed AimigoTutor - tutoring application using multi-modal capabilities
title_sort aimigotutor - tutoring application using multi-modal capabilities
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175732
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