## 5 Co-Saliency
### Methods for Co-Saliency
A three-term energy function refinement model is introduced in order to improve the intra-image smoothness and inter-image consistency. [PERSON] et al. [37] extended the concept of co-saliency from images to videos, and as such operate on multiple input video sequences. They took into consideration both inter-video foreground correspondences and intra-video saliency stimuli, with the objective of ignoring background distraction elements and concurrently emphasizing salient foreground regions. [PERSON] et al. [68] observed that the auxiliary task of co-segmentation improves object boundaries in co-saliency detection, and proposed a joint optimization of the two tasks by solving an energy minimization problem over a graph. The resulting model iteratively transfers useful information in both directions, to improve the prediction of both domains. The solution by [PERSON] et al. [69] produces an initial set of co-saliency maps, which are then refined on object boundaries. The authors then introduced a seed-propagation step over an integrated multilayer graph, aimed at detecting regions missed by lower-level descriptors. Such descriptors are pooled both within-segment and within-group, in order to handle input images having different sizes. Another possible criterion to discriminate among different approaches, is the distinction between deep-learning solutions, and those based on hand-crafted design and traditional techniques. Methods in the deep learning group [67; 70; 94; 95] typically benefit from end-to-end learning, therefore optimizing the final objective of co-saliency estimation regardless of the adopted early-fusion or late-fusion approach. Many are based on the Fully-Convolutional Network (FCN) by [PERSON] et al. [97] or DeepLab by [PERSON] et al. [18], both leveraging the VGG backbone [75]. Other adopted neural architectures include the \"Slow\" CNN-S model by [PERSON] et al. [74]. [PERSON] et al. [67] presented a coarse-to-fine framework for co-saliency detection: they first generate an initial proposal using a mask-guided fully convolutional network, based on the high-level feature response maps of a pre-trained VGG network [75]. They then defined a multi-scale label smoothing model to refine the prediction, optimizing both the label smoothness of pixels and superpixels. [PERSON] et al. [70] presented an end-to-end co-saliency estimation neural network. The model adopts an early-fusion approach by extracting high-level descriptions of the input images, and capturing the group-wise interaction information for group images. It was proven to be able to learn the collaborative relationships between single-image features and group-wise features. [PERSON] et al. [94] presented an original unsupervised approach to co-saliency estimation, addressed in a graphical model based on two losses: the single-image saliency (SIS) loss, acting as the unary term, and the Co-occurrence (COOC) loss, acting as the pairwise term. The authors also presented two refining extensions, namely boundary preservation and map sharpening. [PERSON] et al. [95] presented FASS: a feature-adaptive semi-supervised framework for co-saliency estimation. The proposed solution addresses and exploits the difference in efficacy of image features, by a joint formulation of element-level feature selection and view-level feature weighting. It optimizes co-saliency label prorogation over both labeled and unlabeled image regions. The purely hand-crafted methods include the aforementioned video co-saliency solution by [PERSON] et al. [37], and the more recent work by [PERSON] et al. [92]. Specifically, the latter focuses on predicting the saliency map for one selected key image, and subsequently extending the prediction to other images in the group. The authors proposed fusing individual saliency maps using the \"dense correspondence\" technique, and evaluating a no-reference concentration-based saliency quality to decide whether the fused saliency map improves upon the original one.