  In this work, we contribute to video saliency research in two ways. First, we
introduce a new benchmark for predicting human eye movements during dynamic
scene free-viewing, which is long-time urged in this field. Our dataset, named
DHF1K (Dynamic Human Fixation), consists of 1K high-quality, elaborately
selected video sequences spanning a large range of scenes, motions, object
types and background complexity. Existing video saliency datasets lack variety
and generality of common dynamic scenes and fall short in covering challenging
situations in unconstrained environments. In contrast, DHF1K makes a
significant leap in terms of scalability, diversity and difficulty, and is
expected to boost video saliency modeling. Second, we propose a novel video
saliency model that augments the CNN-LSTM network architecture with an
attention mechanism to enable fast, end-to-end saliency learning. The attention
mechanism explicitly encodes static saliency information, thus allowing LSTM to
focus on learning more flexible temporal saliency representation across
successive frames. Such a design fully leverages existing large-scale static
fixation datasets, avoids overfitting, and significantly improves training
efficiency and testing performance. We thoroughly examine the performance of
our model, with respect to state-of-the-art saliency models, on three
large-scale datasets (i.e., DHF1K, Hollywood2, UCF sports). Experimental
results over more than 1.2K testing videos containing 400K frames demonstrate
that our model outperforms other competitors.
