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Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition.
Shifts in selective visual attention: towards the underlying neural circuitry
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Gradient-based learning applied to document recognition
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Learning to predict where humans look
Judd, T., Ehinger, K., Durand, F., Torralba, A.: · 2009
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Rectified linear units improve restricted boltzmann machines
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Gaze-driven video streaming with saliency-based dual-stream switching
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A benchmark of computational models of saliency to predict human fixations
Judd, T., Durand, F., Torralba, A.: · 2012
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In: Saliency Based Image Cropping. Springer Berlin Heidelberg (2013) 773–782
Ardizzone, E., Bruno, A., Mazzola, G · 2013
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., Zisserman, A.: · 2014
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Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
Vig, E., Dorr, M., Cox, D.: · 2014
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Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
Kümmerer, M., Theis, L., Bethge, M.: · 2015
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Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., Dean, J.: · 2015
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
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SALICON: Saliency in Context
Jiang, M., Huang, S., Duan, J., Zhao, Q.: · 2015
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Learning both weights and connections for efficient neural networks
Han, S., Pool, J., Tran, J., Dally, W.J.: · 2015
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., , Sun, J.: · 2015
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Shallow and deep convolutional networks for saliency prediction
Pan, J., Sayrol, E., Giro-i Nieto, X., McGuinness, K., O’Connor, N.E.: · 2016
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Predicting human eye fixations via an lstm-based saliency attentive model
Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.: · 2016
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What do different evaluation metrics tell us about saliency models?
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., Durand, F.: · 2016
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Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: · 2017
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Borji, A., Itti, L.: · 2015
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SALICON: Reducing the semantic gap in saliency prediction by adapting deep neural networks
Huang, X., Shen, C., Boix, X., Zhao, Q.: · 2015
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Spatio-temporal modeling and prediction of visual attention in graphical user interfaces
Xu, P., Sugano, Y., Bulling, A.: · 2016
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DeepGaze II: Reading fixations from deep features trained on object recognition
Kümmerer, M., Wallis, T.S.A., Bethge, M.: · 2016
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A deep spatial contextual long-term recurrent convolutional network for saliency detection
Liu, N., Han, J.: · 2016
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https://github.com/pytorch
PyTorch
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: · 2017
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DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
Kruthiventi, S.S.S., Ayush, K., Babu, R.V.: · 2017
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Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
Tavakoli, H.R., Borji, A., Laaksonen, J., Rahtu, E.: · 2017
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: · 2017
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Saliency benchmarking: Separating models, maps and metrics
Kümmerer, M., Wallis, T.S.A., Bethge, M.: · 2017
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