CS-VQA: Visual Question Answering with Compressively Sensed Images

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2018, Pages 1283-1287

  Huang, Li Chi ^b   Kulkarni, Kuldeep ^a   Jha, Anik ^b   Lohit, Suhas ^b   Jayasuriya, Suren ^a,b   Turaga, Pavan ^a,b  

^a ARIZONA STATE UNIVERSITY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Compressed sensing; Computer vision; Image reconstruction; Multi layer neural network

Indexed keywords

COMPRESSED SENSING; COMPUTER VISION; DEEP NEURAL NETWORKS; NATURAL LANGUAGE PROCESSING SYSTEMS; NETWORK ARCHITECTURE; NETWORK LAYERS; SEMANTICS; VISUAL LANGUAGES;

COMPRESSED DOMAIN; COMPRESSIVE MEASUREMENTS; NATURAL LANGUAGE PROCESSING; QUESTION ANSWERING; SEMANTIC TASKS; SENSED IMAGE; STATE OF THE ART; VISUAL RECOGNITION;

IMAGE RECONSTRUCTION;

EID: 85062913838     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2018.8451445     Document Type: Conference Paper

References (27)

1. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh, "VQA: Visual question answering, " in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2425-2433.
2. Robert LiKamWa, Zhen Wang, Aaron Carroll, Felix Xiaozhu Lin, and Lin Zhong, "Draining our glass: An energy and heat characterization of google glass, " in Proceedings of 5th Asia-Pacific Workshop on Systems. ACM, 2014, p. 10.
3. Marco F Duarte, Mark A Davenport, Dharmpal Takbar, Jason N Laska, Ting Sun, Kevin F Kelly, and Richard G Baraniuk, "Single-pixel imaging via compressive sampling, " IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 83-91, 2008.
4. David L Donoho, "Compressed sensing, " IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
5. Emmanuel J Candès, Justin Romberg, and Terence Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, " IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489-509, 2006.
6. Mark A. Davenport, Marco F. Duarte, Michael B. Wakin, Jason N. Laska, Dharmpal Takhar, Kevin F. Kelly, and Richard G. Baraniuk, "The smashed filter for compressive classification and target recognition, " in Computational Imaging V, San Jose, CA, USA, January 29-31, 2007, 2007, p. 64980H.
7. Kuldeep Kulkarni and Pavan Turaga, "Reconstruction-free action inference from compressive imagers, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 4, pp. 772-784, 2016.
8. Emmanuel J Candes and Terence Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?, " IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006.
9. Richard G Baraniuk, Volkan Cevher, Marco F Duarte, and Chinmay Hegde, "Model-based compressive sensing, " IEEE Transactions on Information Theory, vol. 56, no. 4, pp. 1982-2001, 2010.
10. Yookyung Kim, Mariappan S Nadar, and Ali Bilgin, "Compressed sensing using a Gaussian scale mixtures model in wavelet domain, " in International Conference on Image Processing. IEEE, 2010, pp. 3365-3368.
11. David L Donoho, Arian Maleki, and Andrea Montanari, "Message-passing algorithms for compressed sensing, " Proceedings of the National Academy of Sciences, vol. 106, no. 45, pp. 18914-18919, 2009.
12. Ali Mousavi, Ankit B Patel, and Richard G Baraniuk, "A deep learning approach to structured signal recovery, " in 53rd Annual Allerton Conference on Communication, Control, and Computing. IEEE, 2015, pp. 1336-1343.
13. Michael Iliadis, Leonidas Spinoulas, and Aggelos K Katsaggelos, "Deep fully-connected networks for video compressive sensing, " Digital Signal Processing, vol. 72, pp. 9-18, 2018.
14. Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, and Amit Ashok, "ReconNet: Non-iterative reconstruction of images from compressively sensed measurements, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 449-458.
15. Suhas Lohit, Kuldeep Kulkarni, and Pavan Turaga, "Direct inference on compressive measurements using convolutional neural networks, " in International Conference on Image Processing. IEEE, 2016, pp. 1913-1917.
16. Kuldeep Kulkarni and Pavan Turaga, "Fast integral image estimation at 1% measurement rate, " arXiv preprint arXiv: 1601. 07258, 2016.
17. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, "Going deeper with convolutions, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.
18. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep residual learning for image recognition, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
19. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean, "Distributed representations of words and phrases and their compositionality, " in Advances in Neural Information Processing Systems, 2013, pp. 3111-3119.
20. Sepp Hochreiter and Jürgen Schmidhuber, "Long short-term memory, " Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
21. Huijuan Xu and Kate Saenko, "Ask, attend and answer: Exploring question-guided spatial attention for visual question answering, " in European Conference on Computer Vision. Springer, 2016, pp. 451-466.
22. Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola, "Stacked attention networks for image question answering, " in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 21-29.
23. Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh, "Hierarchical question-image co-attention for visual question answering, " in Advances In Neural Information Processing Systems, 2016, pp. 289-297.
24. Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach, "Multimodal compact bilinear pooling for visual question answering and visual grounding, " in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, 2016, pp. 457-468.
25. Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al., "Imagenet large scale visual recognition challenge, " International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, 2015.
26. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick, "Microsoft COCO: Common objects in context, " in European Conference on Computer Vision. Springer, 2014, pp. 740-755.
27. Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh, "Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering, " in Conference on Computer Vision and Pattern Recognition (CVPR), 2017.