###### Abstract
Such trending topics are, namely, saliency in omnidirectional images, and multiple-input scenarios, which include co-saliency and video saliency estimation. Although visual saliency has been studied in other fields as well (such as light field and hyper-spectral imaging) most of the current domain-specific research happens to converge on the three mentioned topics, while the literature does not offer enough material to produce a valuable review of recent solutions related to other less widespread domains. Our goal is therefore to highlight the recent trends of research in these fields, providing a concise yet exhaustive insight into each analyzed method, and summarizing the similarities and differences across different solutions. The investigated domains are either very recent, or have lived a particularly dynamic evolution. As a consequence, different methods are typically evaluated and/or optimized on different datasets, making comparative evaluations extremely challenging. Nonetheless, we conduct an analysis on the joint occurrence of methods and datasets, and we benchmark solutions that are directly comparable as they were evaluated in equivalent conditions. Accompanying the development of research into visual saliency estimation through the years, the scientific literature has periodically offered different benchmarks and surveys, typically concerning general-purpose saliency. [PERSON] et al. (2012) [20] provide an in-depth comparison of 35 state of the art methods for saliency estimation, over both synthetic and natural images. A second work by [PERSON] et al. (2015) [9] conduct a similar benchmark including newly developed solutions, the most recent of which, however, was released in the year 2014. A more recent review is presented by [PERSON] et al. (2019) [21], offering an in-depth survey over methods for salient object detection specifically based on deep learning approaches. Concerning domain-specific analyses, [PERSON] et al. (2019-I) [22] cover methods for saliency detection that rely on so-called \"comprehensive information\", such as depth cues, inter-image correspondence (equivalent to co-saliency), and multiple frames. [PERSON] et al. (2018) [23] review the concepts, applications, and challenges intrinsic into co-saliency detection, whereas [PERSON] et al. (2016) [24] focus on video saliency estimation approachesbased on a bottom-up interpretation. With the current survey, our goal is to inform on up-to-date developments in the fields of domain-specific visual saliency estimation. To the best of our knowledge, there are currently no surveys that specifically focus on saliency estimation in omnidirectional images, which is the most recent domain-specific development in the field. The main contributions of this paper are the following:  
* We highlight domains that naturally emerged from a literature review as being particularly timely and relevant. * Through a systematic analysis of the methods in each domain, we show their commonalities and differences. * We provide clear information regarding the targeted ground truth representation, as well as the output that each method can explicitly generate. * We conduct, where deemed fair, a quantitative comparison of the selected methods, and provide some insights on the basis of such comparison. * We report an in-depth analysis of the most common datasets for the analyzed domains, including the representation used for the ground truth saliency information. * We present the commonly used evaluation measures, which can be either domain-specific or general-purpose. * We conclude by synthesizing the emerging directions for research in the specialized literature. The rest of the paper is structured as follows: Section 2 presents the systematic approach that led to the selection of works in this review.