## 6 Video Saliency
### Methods for Video Saliency
With this approach, they were able to eventually produce a generic object segmentation based on the propagation of estimated saliency information. [PERSON] et al. [130] developed a three-step framework. A set of localized estimation models, generated through a random forest regressor, can be first used to create a temporary saliency map. This is then improved through a spatio-temporal refinement step, based on appearance and motion information. The resulting map is finally used to provide saliency cues for the following frame estimation. [PERSON] et al. [121] presented an approach for so-called \"unconstrained video segmentation\". They first generate an initial set of saliency regions through a novel saliency measure. They then compute a homography over optical flow information to retrieve motion cues that are robust to background motion. The two pieces of information can be eventually combined, expanded and refined. [PERSON] et al. [126] developed a super-pixel-based technique that initially produces a prior map for pixel-wise labeling, exploiting a geodesic distance. They then formulated the task as an energy minimization problem, operating on foreground-background models and dynamic location models as unary terms, as well as label smoothness potentials as pairwise terms. [PERSON] et al. [131] designed a method for video saliency detection based on spatio-temporal fusion and low-rank coherency guided diffusion. They first segment the input video into batches, where motion clues are internally diffused. Interbatch saliency priors are then taken into account for a low-level saliency fusion. These clues are eventually used to guide a saliency diffusion step. Similarly to what has been observed with co-saliency and saliency in omnidirectional images, recent methods in the domain of video saliency are also equally spread among hand-crafted solutions [121; 125; 126; 130; 131], and those based on a deep-learning approach [49; 65; 66; 127; 128]. Belonging to the latter category, [PERSON] et al. [49] collected and annotated the DAVSOD dataset (Densely Annotated Video Salient Object Detection), and proposed a neural-network-based approach to video saliency detection that explicitly addresses the problem of \"saliency-shift\" (the phenomenon where human attention switches from one element to another during the stimulus). Their solution is based on convolutional LSTM (Long-short term memory) modules. [PERSON] et al. [127] designed a multi-task neural network for salient object detection in video sequences. The first task, accomplished by the first sub-network, consists of still-image saliency estimation. The second task aims at motion saliency detection based on optical flow images. The two sub-networks were trained end-to-end with the integration of specifically-designed motion-guided attention modules. [PERSON] et al. [128] proposed a solution for video saliency estimation that does not rely on densely-annotated video sequences. They first developed a technique to generate pixel-level pseudo- ground truths from sparsely annotated video frames, based on a neural network operating on optical flow images. They then trained a neural model composed of a spatial refinement network and a spatio-temporal module on their artificially-augmented training data. As mentioned, some solutions target a different representation of video saliency information, namely fixation-related saliency maps. [PERSON] et al. [66] focused on the concept of attentional push:a family of saliency cues that include following the gaze of depicted subjects, accounting for the salient element leaving the scene, and for abrupt scene changes in general. They exploited these concepts to augment a static saliency estimation with the objective of minimizing the relative entropy between estimated and expected fixation patterns. [PERSON] et al.