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Compressive Visual Question Answering.

Arizona State University.

Compressive Visual Question Answering.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Compressive Visual Question Answering./
作者:	Huang, Li-chi.
面頁冊數:	1 online resource (44 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355496239

Compressive Visual Question Answering.
Huang, Li-chi.

Compressive Visual Question Answering. - 1 online resource (44 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Arizona State University, 2017.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355496239Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Compressive Visual Question Answering.
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Pavan Turaga.
502 $a Thesis (M.S.)--Arizona State University, 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0405
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Computer Engineering. $3 1184417
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10634249 $z click for full text (PQDT)

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