Compressive Visual Question Answering
abstract: Compressive sensing theory allows to sense and reconstruct signals/images with lower sampling rate than Nyquist rate. Applications in resource constrained environment stand to benefit from this theory, opening up many possibilities for new applications at the same time. The traditional inf...
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ndltd-asu.edu-item-459522018-06-22T03:08:57Z Compressive Visual Question Answering abstract: Compressive sensing theory allows to sense and reconstruct signals/images with lower sampling rate than Nyquist rate. Applications in resource constrained environment stand to benefit from this theory, opening up many possibilities for new applications at the same time. The traditional inference pipeline for computer vision sequence reconstructing the image from compressive measurements. However,the reconstruction process is a computationally expensive step that also provides poor results at high compression rate. There have been several successful attempts to perform inference tasks directly on compressive measurements such as activity recognition. In this thesis, I am interested to tackle a more challenging vision problem - Visual question answering (VQA) without reconstructing the compressive images. I investigate the feasibility of this problem with a series of experiments, and I evaluate proposed methods on a VQA dataset and discuss promising results and direction for future work. Dissertation/Thesis Huang, Li-chi (Author) Turaga, Pavan (Advisor) Yang, Yezhou (Committee member) Li, Baoxin (Committee member) Arizona State University (Publisher) Computer science Mathematics compressive sensing deep learning visual question anwering eng 44 pages Masters Thesis Computer Engineering 2017 Masters Thesis http://hdl.handle.net/2286/R.I.45952 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2017 |
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English |
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Dissertation |
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Computer science Mathematics compressive sensing deep learning visual question anwering |
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Computer science Mathematics compressive sensing deep learning visual question anwering Compressive Visual Question Answering |
description |
abstract: Compressive sensing theory allows to sense and reconstruct signals/images with lower sampling rate than Nyquist rate. Applications in resource constrained environment stand to benefit from this theory, opening up many possibilities for new applications at the same time. The traditional inference pipeline for computer vision sequence reconstructing the image from compressive measurements. However,the reconstruction process is a computationally expensive step that also provides poor results at high compression rate. There have been several successful attempts to perform inference tasks directly on compressive measurements such as activity recognition. In this thesis, I am interested to tackle a more challenging vision problem - Visual question answering (VQA) without reconstructing the compressive images. I investigate the feasibility of this problem with a series of experiments, and I evaluate proposed methods on a VQA dataset and discuss promising results and direction for future work. === Dissertation/Thesis === Masters Thesis Computer Engineering 2017 |
author2 |
Huang, Li-chi (Author) |
author_facet |
Huang, Li-chi (Author) |
title |
Compressive Visual Question Answering |
title_short |
Compressive Visual Question Answering |
title_full |
Compressive Visual Question Answering |
title_fullStr |
Compressive Visual Question Answering |
title_full_unstemmed |
Compressive Visual Question Answering |
title_sort |
compressive visual question answering |
publishDate |
2017 |
url |
http://hdl.handle.net/2286/R.I.45952 |
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1718701599219515392 |