# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 8 Commonalities and Limitations of the Deep Saliency Models
[121] contains 300 million images, and it performs the target recognition model that is trained on this dataset well. The difference in performance between the use of eye tracking data and similar SALICON data collected with mouse clicks is clearly controversial. 2. Multi-modal approaches: With the development of saliency prediction in the dynamic field, an increasing number of features in different modes, such as vision, hearing, and subtitles, can be used to train models. This multi-modal feature input mode has proven to be an effective way to improve model performance. [PERSON] et al. [122] used audio data to help video prediction. The shared attention proposed by [PERSON] et al. [79] could effectively improve model performance. 3. Visualization: The black box model of deep learning is difficult to present in a manner that humans can understand. However, saliency prediction itself is a representation of visual concepts. Visualized CNNs have many benefits for understanding models, including the meaning of filters, visual patterns, or visual concepts. [PERSON] et al. [123] designed a visual dataset and found that a specific type of database may be better for training. Visualization can help us better understand a model, and it also brings the possibility of proposing better models and databases. 4. Understand high-level semantics: The deep saliency models are good at extracting common features, such as humans and textures, among others. The saliency predictor can also be used to handle these features. However, as shown in Figure 5, the most interesting or significant parts of an image are not necessarily all of these features. Human visual models often entail a reasoning process based on sensory stimuli. To establish the reason behind the relative importance of image regions on the saliency model, researchers can use higher-level features, such as emotions, gaze direction, and body posture. Moreover, aiming to approach the human-level saliency prediction, researchers need to carry out cognitive attention research to help overcome the aforementioned limitations. A few useful explorations have been offered. For example, [PERSON] [98] showed through his experimental results that emotion has a priority effect. Nonetheless, the existing saliency model still cannot fully explain the high-level semantics in the scene. The concept of \"semantic gap\" and the process of determining the relative importance of objects still cannot be resolved; moreover, whether the saliency in natural scenes is guided by objects or low-level features is a matter of debate [124]. The research on the saliency prediction task is closely related to cognitive disciplines, and its findings can help to improve the subsequent various visual research. With the great success of the deep model in saliency prediction, new developments in deep learning have also provided the possibility for new applications and tasks of saliency models. For example, [PERSON] et al. [16] proposed a novel attention-based model for making braking decisions and other driving decisions like steering and acceleration. Figure 5: An animal in the picture attracting more attention than humans. [PERSON] et al. [19] proposed a multimodal salient wave detection network for sleep staging called SalientSleepNet, which translated the time series classification problem into a saliency detection problem and applies it to sleep stage classification. [PERSON] et al. [125] used a saliency model to pursue their research on autism spectrum disorder (ASD). They found that children with ASD, particularly autism, were informed by special objects and less on social objects (e.g., face), and the application of the verification model of obviousness is helpful in monitoring and evaluating their condition. [PERSON] et al. [126] proposed a model for detecting seizure events from raw electroencephalogram (EEG) signals with less dependency on the availability of precise clinical labels. This work opens new avenues for the application of deep learning to neonatal EEG. [PERSON] et al.