# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 7 Performance of Visual Saliency Prediction Models
DeepGaze II ranks first in AUC-Borji with a score of 0.86. Based on the sAUC measure, SALICON performed best with a score of 0.74. The rankings produced by different evaluation methods vary greatly. DeepGaze II and DeepFix perform well in AUC, but other scores are average. Although SAM-ResNet, SAM-VGG, EML-NET and SalGAN did not get the highest score in AUC, these models are outstanding. Table 3 shows the results of the CAT2000 dataset. AUC-Judd is arranged in descending order. Based on the AUC-Judd measure, SAM-ResNet, MSI-Net and SAM-VGG are tied in the top rank with 0.88 (infinite-humans score of 0.90). In the classic model, the performance of BMS is the superior one. Its AUC-Borji score is the highest, and other scores are almost higher than eDN. In general, the models that perform well on the MIT300 dataset also perform well on the CAT2000 dataset. The saliency maps of the model over the CAT2000 database are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model Name** & **AUC-** & **AUC-** & **sAUC** & **SIM** & **EMD** & **CC** & **NSS** & **KL** \\ \hline infinite humans & 0.9 & 0.84 & 0.62 & 1 & 0 & 1 & 2.85 & 0 \\ \hline SAM-ResNet [75] & **0.88** & 0.8 & 0.58 & **0.77** & **1.04** & **0.89** & 2.38 & 0.56 \\ SAM-VGG [75] & **0.88** & 0.79 & 0.58 & 0.76 & 1.07 & **0.89** & 2.38 & 0.54 \\ MSI-Net [82] & **0.88** & 0.82 & 0.59 & 0.75 & 1.07 & 0.87 & 2.3 & 0.36 \\ EML-NET [77] & 0.87 & 0.79 & 0.59 & 0.75 & 1.05 & 0.88 & 2.38 & 0.96 \\ DeepFix [66] & 0.87 & 0.81 & 0.58 & 0.74 & 1.15 & 0.87 & 2.28 & **0.37** \\ BMS [49] & 0.85 & **0.84** & 0.59 & 0.61 & 1.95 & 0.67 & 1.67 & 0.83 \\ eDN [62] & 0.85 & **0.84** & 0.55 & 0.52 & 2.64 & 0.54 & 1.3 & 0.97 \\ iSEEL [106] & 0.84 & 0.81 & 0.59 & 0.62 & 1.78 & 0.66 & 1.67 & 0.92 \\ Judd Model [61] & 0.84 & **0.84** & 0.56 & 0.46 & 3.6 & 0.54 & 1.3 & 0.94 \\ EXYMOL [107] & 0.83 & 0.76 & 0.51 & 0.61 & 1.91 & 0.72 & 1.78 & 1.67 \\ LDS [108] & 0.83 & 0.79 & 0.56 & 0.58 & 2.09 & 0.62 & 1.54 & 0.79 \\ FES [109] & 0.82 & 0.76 & 0.54 & 0.57 & 2.24 & 0.64 & 1.61 & 2.1 \\ Aboudib Magn [110] & 0.81 & 0.77 & 0.55 & 0.58 & 2.1 & 0.64 & 1.57 & 1.41 \\ GBVS [28] & 0.8 & 0.79 & 0.58 & 0.51 & 2.99 & 0.5 & 1.23 & 0.8 \\ Context-Aware saliency & 0.77 & 0.76 & 0.6 & 0.5 & 3.09 & 0.42 & 1.07 & 1.04 \\
[111] & 0.77 & 0.76 & 0.59 & 0.48 & 3.44 & 0.42 & 1.06 & 0.92 \\ IttiKoch [4] & 0.77 & 0.75 & **0.61** & 0.49 & 3.36 & 0.42 & 1.09 & 0.94 \\ AWS [112] & 0.76 & 0.75 & 0.6 & 0.44 & 3.69 & 0.36 & 0.89 & 1.13 \\ AIM [113] & 0.75 & 0.69 & 0.6 & 0.47 & 3.28 & 0.38 & 1.01 & 1.65 \\ Torralba saliency [51] & 0.72 & 0.71 & 0.58 & 0.45 & 3.44 & 0.33 & 0.85 & 1.6 \\ Murray model [72] & 0.7 & 0.7 & 0.59 & 0.43 & 3.79 & 0.3 & 0.77 & 1.14 \\ SUN saliency [54] & 0.7 & 0.69 & 0.57 & 0.43 & 3.42 & 0.3 & 0.77 & 2.22 \\ Achanta [36] & 0.57 & 0.55 & 0.52 & 0.33 & 4.46 & 0.11 & 0.29 & 2.31 \\ IttiKoch [4] & 0.56 & 0.53 & 0.52 & 0.34 & 4.66 & 0.09 & 0.25 & 6.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the static models over the CAT2000 dataset. AUC-Judd, sAUC, NSS, CC, and SIM are used as the five evaluation measures to judge the performance of the model over the DHF1K dataset. The average is taken after calculating the score for each frame. The evaluation results are mainly based on the public results of the DHF1K dataset. The model performance is shown in Table 4.  
Figure 4: Saliency maps over the CAT2000 dataset. STRA -Net ranks first in all ratings, followed by ACLNet. Among the dynamic models, OM-CNN outperforms the other types. Among the static models, the performance of SALICON is superior. The results indicate that the performance of the deep model is better than adding time information to the classic model.