environment. It has been observed that the attention transition is deeply related to the task carried out by the person. Especially in object manipulation tasks, the high-level knowledge of an undergoing task determines a stream of objects or places to be attended successively and thus influences the transition of human attention. For example, to pour water from a bottle to a cup, a person always first looks at the bottle before grasping it and then change the fixation onto the cup during the action of pouring. Therefore, we argue that it is necessary to explore the task-dependent patterns in attention transition in order to achieve accurate gaze prediction.

In this paper, we propose a hybrid gaze prediction model that combines bottom-up visual saliency with task-dependent attention transition learned from successively attended image regions in training data. The proposed model is mainly composed of three modules. The first module generates saliency maps directly from video frames. It is based on a two-stream Convolutional Neural Network (CNN) which is similar to traditional bottom-up saliency prediction models. The second module is based on a recurrent neural network and a fixation state predictor which generates an attention map for each frame based on previously 