Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks

Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model which leverages the semantic modelling power of conditional generative adversari...

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Bibliographic Details
Published in2018 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 1539 - 1548
Main Authors Fernando, Tharindu, Denman, Simon, Sridharan, Sridha, Fookes, Clinton
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Gallium nitride
Generators
Observers
Semantics
Task analysis
Visualization
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Summary:Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model which leverages the semantic modelling power of conditional generative adversarial networks together with memory architectures which capture the subject's behavioural patterns and task dependent factors. We make contributions aiming to bridge the gap between bottom-up feature learning capabilities in modern deep learning architectures and traditional top-down handcrafted features based methods for task specific saliency modelling. The conditional nature of the proposed framework enables us to learn contextual semantics and relationships among di.erent tasks together, instead of learning them separately for each task. Our studies not only shed light on a novel application area for generative adversarial networks, but also emphasise the importance of task specific saliency modelling and demonstrate the plausibility of fully capturing this context via an augmented memory architecture.
DOI:10.1109/WACV.2018.00172