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PREDICTING SALIENCY MAPS FOR ASD PEOPLE

Abstract : This paper presents a novel saliency prediction model for children with autism spectrum disorder (ASD). We design a new convolution neural network and train it with a new ASD dataset. Among the contributions , we can cite the coarse-to-fine architecture as well as the loss function which embeds a regularization term. We also discuss about some data augmentation methods for ASD dataset. Experimental results show that the proposed model performs better than 6 models, one supervised model finetuned with the ASD dataset. Contrary to control people, our results hint that no center bias apply in visuall attention for autistic children.
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https://hal.inria.fr/hal-02264907
Contributor : Olivier Le Meur <>
Submitted on : Wednesday, August 7, 2019 - 6:05:11 PM
Last modification on : Thursday, February 27, 2020 - 1:07:01 AM
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Alexis Nebout, Weijie Wei, Zhi Liu, Lijin Huang, Olivier Le Meur. PREDICTING SALIENCY MAPS FOR ASD PEOPLE. ICME Workshop, Jul 2019, Shanghai, China. ⟨hal-02264907⟩

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