Predicting Saliency Maps for ASD People

Nebout, Alexis; Wei, Weijie; Li, Zhi; Huang, Lijin; Meur, Olivier Le

doi:10.1109/icmew.2019.00121

Cited by 18 publications

(16 citation statements)

References 20 publications

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“…In this section, we first evaluate the ability of aforementioned neurotypical saliency models to predict where ASD people look at. We also consider Nebout's model [8], which is a saliency model dedicated to predict ASD saliency. Figure 4 presents the overall architecture of this model, which is inspired from 3 previous saliency models, namely CASNet model [30], deep gaze network [28] and the multi-level deep network of [29].…”

Section: Performance On Asd Eye Tracking Datasetmentioning

confidence: 99%

“…The two streams are then concatenated into a single stream. Next, More information are given in the paper [8]. In the following subsections, we discuss the performances of these models over the three ASD eye tracking datasets presented in Section II.…”

Section: Performance On Asd Eye Tracking Datasetmentioning

confidence: 99%

“…Figure 3 also shows that the performance of Nebout's model is much higher than neurotypical model (see the third bar for each model) on MIE No dataset. [26], SalGAN [27], DeepGaze II [28], MLNET [29] and Nebout's model [8].…”

Section: ) Performance Of Nebout's Modelmentioning

confidence: 99%

“…Figure5illustrates predicted saliency maps for the tested saliency models, i.e. SAM-Resnet and SAM-VGG[26], SalGAN[27], DeepGaze II[28], MLNET[29] and Nebout's model[8].…”

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confidence: 99%

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From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at

et al. 2020

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Modelling the visual attention of people with autism spectrum disorder (ASD) is attracting more and more interest. This consists in determining where ASD people look and in inferring the important visual features contributing to the gaze deployment. In this paper, we investigate whether or not existing neurotypical as well as ASD saliency models perform well over the whole spectrum of autism. For this purpose, we propose two new eye-tracking datasets of ASD people in order to cover a large part of the autism spectrum, going from high-level functioning (e.g. Asperger) to low-level functioning (e.g. Kanner) autism. We demonstrate that current neurotypical and ASD models do not generalize well and perform well only on a small part of the spectrum. Our objective is to raise the awareness of computer scientists to the difficult task we are facing up when it comes to simulate the gaze deployment of ASD people.

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Section: Performance On Asd Eye Tracking Datasetmentioning

confidence: 99%

Section: Performance On Asd Eye Tracking Datasetmentioning

confidence: 99%

Section: ) Performance Of Nebout's Modelmentioning

confidence: 99%

“…Figure5illustrates predicted saliency maps for the tested saliency models, i.e. SAM-Resnet and SAM-VGG[26], SalGAN[27], DeepGaze II[28], MLNET[29] and Nebout's model[8].…”

mentioning

confidence: 99%

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From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at

et al. 2020

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“…They reported 75% classification accuracy using simple logistic regression. Nebout et al [75] designed a coarse-to-fine convolutional neural network (CNN) to predict saliency maps for ASD children that provides better results than 6 of existing saliency models. This study reported that no center bias is applicable for the visual attention of individuals with ASD, which contradicts the findings of other studies in [26,116].…”

Section: Analyzing Gaze Patternmentioning

confidence: 99%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

Xie

Wang

Webster

et al. 2022

Interdiscip Sci Comput Life Sci

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Predicting Saliency Maps for ASD People

Cited by 18 publications

References 20 publications

From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at

From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

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