## 5 Co-Saliency
### Methods for Co-Saliency  
Table 5 presents a selection of recent methods for co-saliency estimation that were well received by the scientific community. All presented methods target a binary salient object region ground truth. The output of these methods is a continuous-valued saliency map, which is, however, optimized to be as sharp as possible. In some cases [37; 68; 69; 92], the methods also produce a segmentation-oriented binary mask. The co-saliency domain involves, by definition, the analysis of multiple images. How these are handled can help in differentiating among different methods for co-saliency estimation. Early-fusion techniques [70; 89] initially extract a global representation of all the images in the input group, capturing relationships between different images. Conversely, late-fusion techniques [67; 92; 94; 95] are designed \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **CC \(\uparrow\)** & **KL \(\downarrow\)** & **NSS \(\uparrow\)** & **AUC \(\uparrow\)** \\ \hline [PERSON] 2018 [61] & 0.536 & 0.487 & 0.757 & 0.702 \\ [PERSON] 2018 [62] & **0.550** & **0.477** & **0.939** & **0.736** \\ [PERSON] 2018 [63] & 0.527 & 0.698 & 0.851 & 0.714 \\ [PERSON] 2018 [64] & 0.538 & 0.508 & 0.910 & **0.736** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparison of selected methods for saliency estimation in omnidirectional images, on the Salient360\(\uparrow\) challenge dataset. Best results are highlighted in boldface. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Early** & **Late** & **Backbone CNN** & **Deep** & **Hand-** & **Target** & **Output** \\  & **Fusion** & **Fusion** & **Learning** & **Features** & **Crafted** & **Target** & **Output** \\ \hline Cong 2019-II [93] & ✓ & ✓ & (none) & & & ✓ & OR & SM \\ [PERSON] 2019 [67] & & ✓ & FCN (VGG-16) & ✓ & & & OR & SM \\ [PERSON] 2018 [92] & & ✓ & (none) & & & ✓ & OR & SM, BM \\ [PERSON] 2018 [94] & & ✓ & ResNet-50 + FCN (VGG-16) & ✓ & & & OR & SM \\ [PERSON] 2018 [68] & ✓ & ✓ & CNN-S & & ✓ & ✓ & OR & SM, BM \\ [PERSON] 2018 [69] & ✓ & ✓ & DeepLab (VGG-16) & ✓ & & & OR & SM, BM \\ [PERSON] 2018 [95] & & ✓ & FCN-32s (VGG-16) & ✓ & & & OR & SM \\ [PERSON] 2018 [96] & ✓ & ✓ & VGG-16 & & ✓ & ✓ & OR & SM \\ [PERSON] 2017-I [37] & ✓ & ✓ & (none) & & & ✓ & OR & SM, BM \\ [PERSON] 2017 [70] & ✓ & & FCN (VGG-16) & ✓ & & & OR & SM \\ [PERSON] 2017 [89] & ✓ & & (none) & & ✓ & ✓ & OR & SM \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics of the analyzed methods for co-saliency estimation. The “Target” column indicates the nature of the ground truth used to train or develop the methods, while “Output” describes what data they are explicitly able to generate (OR = Object Regions, SM = Sharp saliency Maps, BM = Binary Masks). to estimate single-image saliency from each input individually, and reciprocally update them in a second phase, based on the extracted information. Joining the efforts of early and late fusion techniques, are methods that exploit both approaches by extracting so-called \"intra-image saliency\" (i.e., from each individual image) as well as \"inter-image saliency\" (as the correspondence among multiple images), to eventually combine them [37; 68; 69; 96]. [PERSON] et al. (2018)[96] proposed computing intra-saliency maps exploiting the depth information associated with each image, and calculating the inter-saliency maps based on multi-constraint feature matching to improve the overall performance. A cross-label-propagation scheme was adopted to optimize and refine both maps in a cross-way, eventually integrated into a final co-saliency map. In a subsequent work, [PERSON] et al. (2019-II) [93] formulated the inter-image correspondence as a hierarchical sparsity reconstruction framework. They addressed image-pairs correspondences through a set of pairwise dictionaries, and global image group characteristics through a ranking-scheme-based common dictionary.