Keywords
saliency, deep learning, transfer learning, probabilistic modelling
Abstract
When free-viewing scenes, the first few fixations of human observers are driven in part by bottom-up attention. We seek to characterize this process by extracting all information from images that can be used to predict fixation densities (Kuemmerer et al, PNAS, 2015). If we ignore time and observer identity, the average amount of information is slightly larger than 2 bits per image for the MIT 1003 dataset. The minimum amount of information is 0.3 bits and the maximum 5.2 bits. Before the rise of deep neural networks the best models were able to capture 1/3 of this information on average. We developed new saliency algorithms based on high-performing convolutional neural networks such as AlexNet or VGG-19 that have been shown to provide generally useful representations of natural images. Using a transfer learning paradigm we first developed DeepGaze I based on AlexNet that captures 56% of the total information. Subsequently, we developed DeepGaze II based on VGG-19 that captures 88% and is state-of-the-art on the MIT 300 benchmark dataset. We will show best case and worst case examples as well as feature selection methods to visualize which structures in the image are critical for predicting fixation densities.
Start Date
11-5-2016 11:35 AM
End Date
11-5-2016 12:00 PM
Included in
Using Deep Features to Predict Where People Look
When free-viewing scenes, the first few fixations of human observers are driven in part by bottom-up attention. We seek to characterize this process by extracting all information from images that can be used to predict fixation densities (Kuemmerer et al, PNAS, 2015). If we ignore time and observer identity, the average amount of information is slightly larger than 2 bits per image for the MIT 1003 dataset. The minimum amount of information is 0.3 bits and the maximum 5.2 bits. Before the rise of deep neural networks the best models were able to capture 1/3 of this information on average. We developed new saliency algorithms based on high-performing convolutional neural networks such as AlexNet or VGG-19 that have been shown to provide generally useful representations of natural images. Using a transfer learning paradigm we first developed DeepGaze I based on AlexNet that captures 56% of the total information. Subsequently, we developed DeepGaze II based on VGG-19 that captures 88% and is state-of-the-art on the MIT 300 benchmark dataset. We will show best case and worst case examples as well as feature selection methods to visualize which structures in the image are critical for predicting fixation densities.