S. Fenty, T. Houp, and L. Taylor, Webcomics: The influence and continuation of the comix revolution, ImageTexT: Interdisciplinary Comics Studies, vol.1, issue.2, 2004.

M. J. Green and K. R. Myers, Graphic medicine: use of comics in medical education and patient care, BMJ: British Medical Journal (Online), vol.340, 2010.

A. Farthing and E. Priego, graphic medicineas a mental health information resource: Insights from comics producers, The Comics Grid: Journal of Comics Scholarship, vol.6, 2016.

M. Tatalovic, Science comics as tools for science education and communication: a brief, exploratory study, Jcom, vol.8, issue.4, 2009.

L. Kruger and P. W. Shariff, shoothis book makes me to think! education, entertainment, and life-skills comics in south africa, Poetics Today, vol.22, issue.2, pp.475-513, 2001.

G. E. Schwarz, Graphic novels for multiple literacies, Journal of Adolescent & Adult Literacy, vol.46, issue.3, pp.262-265, 2002.

L. Gonick, The Cartoon History of the Universe: Volumes 1-7: From the Big Bang to Alexander the Great, Crown/Archetype, vol.1, 2014.

N. Cohn, The Visual Language of Comics: Introduction to the Structure and Cognition of Sequential Images, 2013.

A. Farthing, Illustrating cognition-a review of the visual language of comics, The Comics Grid: Journal of Comics Scholarship, vol.4, issue.1, 2014.

A. Farthing and E. Priego, Data from graphic medicineas a mental health information resource: Insights from comics producers, Open Health Data, vol.4, issue.1, 2016.

A. Santella, M. Agrawala, D. Decarlo, D. Salesin, and M. Cohen, Gaze-based interaction for semi-automatic photo cropping, Proceedings of the SIGCHI conference on Human Factors in computing systems, pp.771-780, 2006.

I. Thirunarayanan, K. Khetarpal, S. Koppal, O. Le-meur, J. Shea et al., Creating segments and effects on comics by clustering gaze data, ACM Transactions on Multimedia Computing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01650400

E. Jain, Y. Sheikh, and J. Hodgins, Predicting moves-on-stills for comic art using viewer gaze data, IEEE CGA Special Issue on Quality Assessment and Perception in Computer Graphics, 2016.

S. J. Pan and Q. Yang, A survey on transfer learning, IEEE Transactions on knowledge and data engineering, vol.22, issue.10, pp.1345-1359, 2010.

A. Borji and L. Itti, Cat2000: A large scale fixation dataset for boosting saliency research, 2015.

C. Shen and Q. Zhao, Webpage saliency, 2014.

O. , L. Meur, and Z. Liu, Saccadic model of eye movements for free-viewing condition, Vision research, vol.116, pp.152-164, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01204682

Z. Bylinskii, T. Judd, A. Borji, L. Itti, F. Durand et al., Mit saliency benchmark, 2015.

M. Kümmerer, T. S. Wallis, and M. Bethge, Deepgaze ii: Reading fixations from deep features trained on object recognition, 2016.

M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, Multilevel net: A visual saliency prediction model, European Conference on Computer Vision, pp.302-315, 2016.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

H. R. Tavakoli, A. Borji, J. Laaksonen, and E. Rahtu, Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features, Neurocomputing, vol.244, pp.10-18, 2017.

J. Harel, C. Koch, and P. Perona, Graph-based visual saliency, Advances in neural information processing systems, pp.545-552, 2007.

L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell, Sun: A bayesian framework for saliency using natural statistics, Journal of vision, vol.8, issue.7, pp.32-32, 2008.

N. D. Bruce and J. K. Tsotsos, Saliency, attention, and visual search: An information theoretic approach, Journal of vision, vol.9, issue.3, pp.5-5, 2009.

N. Murray, M. Vanrell, X. Otazu, and C. A. Parraga, Saliency estimation using a non-parametric low-level vision model, Computer vision and pattern recognition (cvpr), pp.433-440, 2011.

A. Garcia-diaz, X. R. Fdez-vidal, X. M. Pardo, and R. Dosil, Saliency from hierarchical adaptation through decorrelation and variance normalization, Image and Vision Computing, vol.30, issue.1, pp.51-64, 2012.

N. Riche, M. Mancas, M. Duvinage, M. Mibulumukini, B. Gosselin et al., Rare2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis, Signal Processing: Image Communication, vol.28, issue.6, pp.642-658, 2013.

J. Pan, E. Sayrol, X. Giro-i-nieto, K. Mcguinness, and N. E. O'connor, Shallow and deep convolutional networks for saliency prediction, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.598-606, 2016.

M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, Predicting human eye fixations via an lstm-based saliency attentive model, 2016.

J. Pan, C. Canton, K. Mcguinness, N. E. O'connor, J. Torres et al., Salgan: Visual saliency prediction with generative adversarial networks, 2017.

O. , L. Meur, and T. Baccino, Methods for comparing scanpaths and saliency maps: strengths and weaknesses, Behavior Research Method, vol.45, issue.1, pp.251-266, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00757615

W. Shan, G. Sun, X. Zhou, and Z. Liu, Two-stage transfer learning of end-to-end convolutional neural networks for webpage saliency prediction, International Conference on Intelligent Science and Big Data Engineering, pp.316-324, 2017.

K. Khetarpal and E. Jain, A preliminary benchmark of four saliency algorithms on comic art, 2016 IEEE International Conference on, pp.1-6, 2016.

O. , L. Meur, and A. Coutrot, Introducing context-dependent and spatially-variant viewing biases in saccadic models, Vision research, vol.121, pp.72-84, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01391745