# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 5 Visual Saliency Prediction Datasets
### Static Datasets  
* TORONTO dataset: In 2006, [PERSON] et al. [41] established the TORONTO dataset. It is one of the earliest and most widely used datasets of computer vision. It includes 120 color images with a resolution of 511 \(\times\) 681. The images contain indoor and outdoor scenes and a total of 20 recorded observers' eye movement data. * MIT300 dataset: In 2012, [PERSON] et al. [70] of MIT established the MIT300 dataset. It contains 300 natural images from Flickr's creation and sharing and the eye movement data of 39 observers. At that time, the MIT300 dataset was the most influential and most widely used in the dataset saliency field. The dataset is generally not used as a training set. However, the model comprising the MIT300 dataset can be evaluated. * MIT1003 dataset: The MIT1003 dataset was also established by [PERSON] et al. [61]. It contains a total of 1003 images from Flickr's collection of images and the LabelMe website and the eye movement data of 15 observers. The MIT1003 dataset can be regarded as a supplement to the MIT300 dataset. The MIT1003 and MIT300 datasets can be used as a training set and a test set for performance evaluation, respectively. * DUT-OMRON dataset: In 2013, [PERSON] et al. [95] established the DUT-OMRON dataset. It contains 5168 images, and each image provides eye movement data of 5 observers. This dataset is annotated with eye movement data, but it mainly focuses on salient object detection, with one or more salient objects and a relatively complex background. * CAT2000 dataset: The CAT2000 dataset was established by [PERSON] et al. [96]. It contains 2000 images under free observation by 24 observers. Twenty scenes are categorized as cartoon, art, indoor, and outdoor scenes. These categories contain bottom-up attention cues and top-down factors. The different types of images are suitable for a variety of attention behavior studies. * SALICON dataset: In 2015, [PERSON] et al. [69] established the SALICON dataset. This large mouse tracking dataset for contextual saliency was established by selecting  
Figure 3: Development process of deep saliency prediction models. 20,000 images in MS-COCO. It is currently the largest attention dataset in terms of scale and context variability. The difference from the abovementioned databases is that the SALICON dataset does not use an eye tracker to record eye movement data but rather uses the Amazon Mechanical Turk platform; however, the eye movement data recorded by the mouse was used to evaluate the performance of the model. [PERSON] et al. [97] emphasized that problems may arise in evaluating model performance when eye movement data are recorded by the mouse. Nonetheless, the SALICON dataset is the largest dataset in the current field, and it continues to be widely used by current mainstream saliency prediction models based on deep learning technology. The SALICON dataset offers eye movement data for the training set (10,000 pictures) and validation set (5000 pictures), and it can retain the eye movement data of the test set (5000 pictures). * EMOd dataset: The EMOd dataset is a new dataset proposed by [PERSON] et al. [98]. It contains 1019 emotional images with target-level and image-level annotations. It was designed for studying visual saliency and image emotion. In the image labeling process of the EMOd dataset, the main target objects in each image are labeled with attributes, such as target contour, target name, emotional category (negative, neutral, or positive), and semantic category. The four semantic categories are as follows: the target directly related to humans, the target related to human non-visual perception, the target designed to attract attention or interact with humans, and the target with implicit signs. Each target is coded to have one or more categories. Furthermore, the EMOd dataset has a total of 4302 targets with fine contours, emotional labels, and semantic labels. The number of positive, neutral, and negative targets are 839, 2429, and 1034, respectively.