Details

Autor(en) / Beteiligte

• Kruthiventi, Srinivas S. S.
• Ayush, Kumar
• Babu, R. Venkatesh

Titel

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations

Ist Teil von

• IEEE transactions on image processing, 2017-09, Vol.26 (9), p.4446-4456

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2017

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.