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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 questio... (more)
Created Date 2017
Contributor Huang, Li-chi (Author) / Turaga, Pavan (Advisor) / Yang, Yezhou (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Subject Computer science / Mathematics / compressive sensing / deep learning / visual question anwering
Type Masters Thesis
Extent 44 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis