TRANSFER LEARNING WITH DEEP NETWORKS FOR SALIENCY PREDICTION IN NATURAL VIDEO

The main purpose of transfer learning is to resolve the problem of different data distribution, generally, when the training samples of source domain are different from the training samples of the target domain. Prediction of salient areas in natural video suffers from the lack of large video benchmarks with human gaze fixations. Different databases only provide dozens up to one or two hundred of videos. The only public large database is HOLLYWOOD with 1707 videos available with gaze recordings. The main idea of this paper is to transfer the knowledge learned with the deep network on a large dataset to train the network on a small dataset to predict salient areas. The results show an improvement on two small publicly available video datasets.