@inproceedings{8ac6075ddec74e4eb4b2cd2a2a0dd1ed,
title = "CS-VQA: Visual Question Answering with Compressively Sensed Images",
abstract = "Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop a series of deep-network architectures that exploit available compressive data to increasing degrees of accuracy, and show that VQA is indeed solvable in the compressed domain. Our results show that there is nominal degradation in VQA performance when using compressive measurements, but that accuracy can be recovered when VQA pipelines are used in conjunction with state-of-the-art deep neural networks for CS reconstruction. The results presented yield important implications for resource-constrained VQA applications.",
keywords = "Compressed sensing, Computer vision, Image reconstruction, Multi-layer neural network",
author = "Huang, {Li Chi} and Kuldeep Kulkarni and Anik Jha and Suhas Lohit and Suren Jayasuriya and Pavan Turaga",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451445",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1283--1287",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
}