Deep Visual Attention Prediction

In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve the CNN-based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction…

• NN
  National Natural Science Foundation of China

  Award: 61272359
• BM
  Beijing Municipal Commission of Education
• NS
  Natural Science Foundation of Beijing Municipality

  Award: 4182056
• NK
  National Key Research and Development Program of China

  Award: 2013CB328805