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    A Novel Framework for Robustness Analysis of Visual QA Models

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    Name:
    VQA_Robustness_AAAI2019.pdf
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    1.148Mb
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Huang, Jia-Hong cc
    Dao, Cuong Duc
    Alfadly, Modar cc
    Ghanem, Bernard cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Science and Engineering
    Earth Science and Engineering Program
    Electrical Engineering Program
    Physical Science and Engineering (PSE) Division
    VCC Analytics Research Group
    Date
    2019-07-17
    Permanent link to this record
    http://hdl.handle.net/10754/662272
    
    Metadata
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    Abstract
    Deep neural networks have been playing an essential role in many computer vision tasks including Visual Question Answering (VQA). Until recently, the study of their accuracy was the main focus of research but now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating their tolerance to varying noise levels. In VQA, adversarial attacks can target the image and/or the proposed main question and yet there is a lack of proper analysis of the later. In this work, we propose a flexible framework that focuses on the language part of VQA that uses semantically relevant questions, dubbed basic questions, acting as controllable noise to evaluate the robustness of VQA models. We hypothesize that the level of noise is negatively correlated to the similarity of a basic question to the main question. Hence, to apply noise on any given main question, we rank a pool of basic questions based on their similarity by casting this ranking task as a LASSO optimization problem. Then, we propose a novel robustness measure Rscore and two largescale basic question datasets (BQDs) in order to standardize robustness analysis for VQA models.
    Citation
    Huang, J.-H., Dao, C. D., Alfadly, M., & Ghanem, B. (2019). A Novel Framework for Robustness Analysis of Visual QA Models. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8449–8456. doi:10.1609/aaai.v33i01.33018449
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and used the resources of the Supercomputing Laboratory at KAUST in Thuwal, Saudi Arabia.
    Publisher
    Association for the Advancement of Artificial Intelligence (AAAI)
    Journal
    Proceedings of the AAAI Conference on Artificial Intelligence
    Conference/Event name
    AAAI Conference on Artificial Intelligence
    DOI
    10.1609/aaai.v33i01.33018449
    arXiv
    1711.06232
    Additional Links
    http://aaai.org/ojs/index.php/AAAI/article/view/4861
    ae974a485f413a2113503eed53cd6c53
    10.1609/aaai.v33i01.33018449
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Computer Science Program; Electrical and Computer Engineering Program; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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