# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 7 Performance of Visual Saliency Prediction Models  
The MIT benchmark has the most comprehensive saliency model and evaluation benchmark. In this chapter, the static image performance evaluation results of the models in the MIT300 and CAT2000 datasets are selected over the MIT benchmark. Then, the performance of the dynamic model is selected over the DHF1K dataset. The data have been obtained from the running results of the MIT benchmark, the author's study, and the author's program on GitHub. The MIT benchmark has a total of eight evaluation measures (including three AUC variants). A total of 93 static models are evaluated. The following 16 models with much better performance are selected for comparison: eDN, Deep Gaze I, Deep Gaze II, DeepFix, SALICON, SalNet, ML-Net, SalGAN, EML-Net, SAM-VGG, SAM-ResNet, AIM, Judd Model, GBVS, ITTI, and SUN. In addition, MIT also considers five baselines. One of these baselines, namely, the infinite humans, is used as the reference measure. The infinite-humans baseline can simulate the gaze point under the observation of infinite people, which is similar to the highest score. The obtained results are shown in Table 2. The best indicators are marked in bold. Thus far, the CAT2000 dataset comprises a total of 31 evaluated models, 10 of which are neural network-based models. The obtained results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **Location Based** & \
\begin{tabular}{c} **Distribution** \\ **Based** \\ \end{tabular} & **Similarity** &
\begin{tabular}{c} **Continuous** \\ **Ground-Truth** \\ \end{tabular} \\ \hline AUC-Judd & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ AUC-Borji & \(\surd\) & & \(\surd\) & & \\ sAUC & \(\surd\) & & \(\surd\) & & \\ EMD & & \(\surd\) & & \(\surd\) \\ NSS & \(\surd\) & & \(\surd\) & & \\ CC & & \(\surd\) & \(\surd\) & \(\surd\) \\ SIM & & \(\surd\) & \(\surd\) & \(\surd\) \\ KL & & \(\surd\) & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of evaluation measures for visual saliency prediction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Name** & \
\begin{tabular}{c} **AUC-** \\ **Judd** \\ \end{tabular} &
\begin{tabular}{c} **AUC-** \\ **Borji** \\ \end{tabular} & **sAUC** & **SIM** & **EMD** & **CC** & **NSS** & **KL** \\ \hline infinite humans & 0.92 & 0.88 & 0.81 & 1 & 0 & 1 & 3.29 & 0 \\ \hline Deep Gaze II [68] & **0.88** & **0.86** & 0.72 & 0.46 & 3.98 & 0.52 & 1.29 & 0.96 \\ EML-NET [77] & **0.88** & 0.77 & 0.7 & **0.68** & **1.84** & **0.79** & **2.47** & 0.84 \\ DeepFix [66] & 0.87 & 0.8 & 0.71 & 0.67 & 2.04 & 0.78 & 2.26 & 0.63 \\ SALICON [69] & 0.87 & 0.85 & **0.74** & 0.6 & 2.62 & 0.74 & 2.12 & **0.54** \\ SAM-ResNet [75] & 0.87 & 0.78 & 0.7 & 0.68 & 2.15 & 0.78 & 2.34 & 1.27 \\ SAM-VGG [75] & 0.87 & 0.78 & 0.71 & 0.67 & 2.14 & 0.77 & 2.3 & 1.13 \\ SalGAN [76] & 0.86 & 0.81 & 0.72 & 0.63 & 2.29 & 0.73 & 2.04 & 1.07 \\ ML-Net [74] & 0.85 & 0.75 & 0.7 & 0.59 & 2.63 & 0.67 & 2.05 & 1.1 \\ Deep Gaze I [63] & 0.84 & 0.83 & 0.66 & 0.39 & 4.97 & 0.48 & 1.22 & 1.23 \\ SalNet [71] & 0.83 & 0.82 & 0.69 & 0.52 & 3.31 & 0.58 & 1.51 & 0.81 \\ eDN [62] & 0.82 & 0.81 & 0.62 & 0.41 & 4.56 & 0.45 & 1.14 & 1.1 \\ Judd Model [61] & 0.81 & 0.8 & 0.6 & 0.42 & 4.45 & 0.47 & 1.18 & 1.12 \\ GBVS [28] & 0.81 & 0.8 & 0.63 & 0.48 & 3.51 & 0.48 & 1.24 & 0.87 \\ AIM [41] & 0.77 & 0.75 & 0.66 & 0.4 & 4.73 & 0.31 & 0.79 & 1.18 \\ IttiKoch2 [4] & 0.75 & 0.74 & 0.63 & 0.44 & 4.26 & 0.37 & 0.97 & 1.03 \\ SUN saliency [54] & 0.67 & 0.66 & 0.61 & 0.38 & 5.1 & 0.25 & 0.68 & 1.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the static models over the MIT300 dataset. Table 2 shows the results of the MIT300 dataset. The AUC-Judd index is arranged in descending order. The top models are all based on deep learning. EML-NET performed best, and it got the highest scores under a variety of measures. Based on the AUC-Judd measure, DeepGaze II and EML-NET are in the top two ranks with a score of 0.88.