Video Saliency Detection using Deep Learning

Abstract: A deep learning model for video saliency detection is proposed and trained. The neural network architecture combines recent innovations in the field: A twostream approach merges two separate input streams for appearance and motion aspects of saliency. Pre-trained convolutional features detect objectness. Attention modules are employed to efficiently reweight features. A ConvLSTM module ensures temporal consistency. Training data comprises both videos and images with corresponding gaze fixation locations from eye trackers. The model is evaluated and shown to perform on par with the state of the art.