# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
###### Abstract  
The human attention mechanism can be understood and simulated by closely associating the saliency prediction task to neuroscience and psychology. Furthermore, saliency prediction is widely used in computer vision and interdisciplinary subjects. In recent years, with the rapid development of deep learning, deep models have made amazing achievements in saliency prediction. Deep learning models can automatically learn features, thus solving many drawbacks of the classic models, such as handcrafted features and task settings, among others. Nevertheless, the deep models still have some limitations, for example in tasks involving multi-modality and semantic understanding. This study focuses on summarizing the relevant achievements in the field of saliency prediction, including the early neurological and psychological mechanisms and the guiding role of classic models, followed by the development process and data comparison of classic and deep saliency prediction models. This study also discusses the relationship between the model and human vision, as well as the factors that cause the semantic gaps, the influences of attention in cognitive research, the limitations of the saliency model, and the emerging applications, to provide new saliency predictions for follow-up work and the necessary help and advice. visual attention; visual saliency; saliency prediction; deep learning +
Footnote †: journal:  
**C****C**: **Applied**  
Received: 20 November 2021  
Accepted: 21 December 2021  
Published: 29 December 2021  
**Published: 29 December 2021**
model based on the bottom-up model, from which the deep learning models have gradually flourished. Compared with the classic models, the performance of these newly developed models has been greatly improved, and the performance is gradually approaching the human inter-observer. The significance of the research on visual saliency detection lies in two aspects: first, as a verifiable prediction, it can be used as a model-based hypothesis test to understand human attention mechanisms at the behavioral and neural levels. Second, the saliency prediction model based on the attention mechanism has been widely used in numerous ways, such as target prediction [4], target tracking [5], image segmentation [6], image classification [7], image stitching [8], video surveillance [9], image or video compression [10], image or video retrieval [11], salient object detection [12], video segmentation [13], image cropping [14], visual SLAM (Simultaneous Localization and Mapping) [15], end-to-end driving [16], video question answering [17], medical diagnosis [18], health monitoring [19] and so on. The current research on saliency detection mainly involves two types of tasks, namely, saliency prediction (or eye fixation prediction) and Salient Object Detection (SOD). Both types of tasks aim to detect the most significant area of a picture or a video. However, differences exist between these two models and their application scenarios. Saliency prediction is informed by the human visual attention mechanism and predicts the possibility of the human eyes to stay in a certain position in the scene. By contrast, salient object detection, as the other branch, focuses on the perception and description of the object level, which is a pure computer vision task. The two types of tasks asre shown in Figure 1.  
Numerous researchers have recently investigated SOD tasks. Presumably, as a pure visual task, SOD can be more easily and directly applied to certain visual scenes, which is more driven by applications in different fields. Benefiting from large-scale benchmarking and deep learning, SOD has been developing rapidly and has shown amazing achievements [20]. In recent years, many researchers have made outstanding contributions. [PERSON] et al. [21] proposed a general framework using iterative top-down and bottom-up saliency inference. In addition, the framework used parameter sharing and weight sharing to reduce the amount of parameters. Besides, [PERSON] et al.