Video Salient Object Detection via Fully Convolutional Networks

This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video…

• NN
  National Natural Science Foundation of China

  Awards: 2013CB328805, 61272359
• BM
  Beijing Municipal Commission of Education
• NK
  National Key Research and Development Program of China

  Award: 2013CB328805