Question-answering on image/video content
|
Muhammad Iqbal Hasan Chowdhury Thesis
(PDF 12MB)
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis explores a computer's ability to understand multimodal data where the correspondence between image/video content and natural language text are utilised to answer open-ended natural language questions through question-answering tasks. Static image data consisting of both indoor and outdoor scenes, where complex textual questions are arbitrarily posed to a machine to generate correct answers, was examined. Dynamic videos consisting of both single-camera and multi-camera settings for the exploration of more challenging and unconstrained question-answering tasks were also considered. In exploring these challenges, new deep learning processes were developed to improve a computer's ability to understand and consider multimodal data.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 205096 |
|---|---|
| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Fookes, Clinton & Sridharan, Sridha |
| Keywords: | Visual Question Answering (VQA), Deep Learning, Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), Generative Adversarial Networks (GAN), Relation Network |
| DOI: | 10.5204/thesis.eprints.205096 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Electrical Engineering & Robotics |
| Institution: | Queensland University of Technology |
| Deposited On: | 13 Nov 2020 00:43 |
| Last Modified: | 09 Dec 2020 00:42 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page