We present a new computational model for gaze prediction in egocentric videos
by exploring patterns in temporal shift of gaze fixations (attention
transition) that are dependent on egocentric manipulation tasks. Our assumption
is that the high-level context of how a task is completed in a certain way has
a strong influence on attention transition and should be modeled for gaze
prediction in natural dynamic scenes. Specifically, we propose a hybrid model
based on deep neural networks which integrates task-dependent attention
transition with bottom-up saliency prediction. In particular, the
task-dependent attention transition is learned with a recurrent neural network
to exploit the temporal context of gaze fixations, e.g. looking at a cup after
moving gaze away from a grasped bottle. Experiments on public egocentric
activity datasets show that our model significantly outperforms
state-of-the-art gaze prediction methods and is able to learn meaningful
transition of human attention.

通过探索注意转移中的时间偏移模式（注意转换）来提出一种新的计算模型，它取决于自我操作任务，以在自然动态场景中推测视线， 并结合深度神经网络和进阶的注意转移和底层显著性预测，利用视线的时间上下文，能够显著优于其他预测方法。