# Advancing in RGB-D Salient Object Detection: A Survey
## 4 Performance Evaluation and Comparison
### Comparing with Prior Art
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Time** & **Name** & **Backbone** & \
\begin{tabular}{l} **Attention** \\ **Mechanism** \\ \end{tabular} &
\begin{tabular}{l} **Multi-Model** \\ **Fusion** \\ \end{tabular} & \(\mathbf{F}_{\beta}\) & \(\mathbf{S}_{\alpha}\) & \(\mathbf{E}_{\phi}\) & **MAE** \\ \hline
2020 & ATSA [73] & VGG-19 & ✓ & ✓ & 0.893 & 0.901 & 0.921 & 0.040 \\  & cmMS [74] & VGG-16 & ✗ & ✓ & 0.897 & 0.900 & 0.936 & 0.044 \\  & D\({}^{3}\)Net [49] & VGG-16 & ✗ & ✓ & 0.887 & 0.893 & 0.930 & 0.051 \\  & ASIFNet [75] & VGG-16 & ✓ & ✓ & 0.901 & 0.889 & - & 0.047 \\  & DRLF [76] & VGG-16 & ✗ & ✓ & 0.883 & 0.886 & 0.926 & 0.550 \\ \hline
2021 & RD3D [77] & ResNet-50 & ✗ & ✓ & 0.914 & 0.916 & 0.947 & 0.036 \\  & MCINet [78] & ResNet-50 & ✗ & ✓ & 0.902 & 0.906 & 0.939 & 0.039 \\  & SwinNet [7] & ResNet-50, ResNet-101 & ✓ & ✓ & 0.922 & 0.935 & 0.934 & 0.027 \\  & BiANet [35] & VGG-16 & ✓ & ✓ & 0.920 & 0.915 & 0.948 & 0.039 \\  & BTSNet [79] & ResNet-50 & ✗ & ✓ & 0.924 & 0.921 & 0.954 & 0.036 \\  & DSNet [80] & ResNet-50 & ✗ & ✓ & 0.895 & 0.921 & 0.945 & 0.034 \\  & SPNet [81] & ResNet-50 & ✗ & ✓ & 0.935 & 0.925 & 0.954 & 0.028 \\  & EBFNet [13] & VGG-16 & ✗ & ✓ & 0.895 & 0.907 & 0.936 & 0.038 \\  & CMINet [12] & ResNet-50 & ✗ & ✓ & 0.925 & 0.939 & 0.956 & 0.032 \\  & CDINet [82] & VGG-16 & ✗ & ✓ & 0.922 & 0.919 & - & 0.036 \\  & CCAFNet [83] & - & ✗ & ✓ & 0.911 & 0.910 & 0.920 & 0.037 \\  & DSAF\({}^{3}\)F [36] & VGG-19 & ✓ & ✓ & 0.892 & 0.918 & 0.950 & 0.024 \\ \hline
2022 & C\({}^{2}\)DFNet [84] & ResNet-50 & ✗ & ✓ & 0.937 & 0.922 & 0.948 & 0.020 \\  & DCMF [85] & VGG-16, ResNet-50 & ✗ & ✓ & 0.913 & 0.922 & 0.940 & 0.029 \\  & MVSalNet [86] & ResNet-50 & ✗ & ✓ & 0.942 & 0.937 & 0.973 & 0.019 \\  & SPSN [87] & ResNet-50 & ✗ & ✗ & 0.942 & 0.937 & 0.973 & 0.017 \\  & CIRNet [88] & RevVGG & ✗ & ✓ & 0.927 & 0.925 & 0.925 & 0.035 \\  & BPGNet [89] & VGG-16 & ✗ & ✓ & 0.926 & 0.923 & 0.953 & 0.034 \\  & ZoomNet [90] & ResNet-50 & ✗ & ✓ & 0.926 & 0.914 & 0.940 & 0.037 \\  & MIADPD [91] & ResNet-50 & ✓ & ✓ & 0.916 & 0.914 & 0.951 & 0.036 \\ \hline
2023 & HINet [92] & ResNet-50 & ✗ & ✓ & 0.914 & 0.915 & 0.945 & 0.039 \\  & HiDAnet [40] & VGG-16, ResNet-50 & ✓ & ✓ & 0.927 & 0.928 & 0.962 & 0.021 \\  & CATNet [24] & Swin-B & ✓ & ✓ & 0.929 & 0.937 & 0.933 & 0.025 \\  & EGANNet [93] & - & ✗ & ✓ & 0.883 & - & 0.922 & 0.033 \\  & CAVER [94] & ResNet-50, ResNet-101 & ✗ & ✓ & 0.928 & 0.926 & 0.958 & 0.030 \\  & ICON [95] & VGG, PVT, ResNet & ✗ & ✓ & 0.891 & 0.893 & 0.937 & 0.051 \\  & LENO [96] & ResNet-50 & ✓ & ✗ & 0.824 & 0.838 & 0.888 & 0.073 \\  & DCMNet [97] & Res2 Net & ✓ & ✓ & 0.899 & - & 0.920 & 0.036 \\  & MPDNet [33] & ResNet-50 & ✓ & ✗ & 0.912 & 0.912 & 0.937 & 0.041 \\  & XMSNet [98] & PVT & ✓ & ✓ & 0.942 & 0.931 & 0.960 & 0.025 \\ \hline
2024 & UTDNet [99] & VGG-16, ResNet-50 & ✗ & ✓ & 0.925 & 0.923 & 0.948 & 0.036 \\  & DCMTrans [45] & T2T-14 & ✓ & ✓ & 0.934 & 0.932 & 0.959 & 0.031 \\  & EMTrans [100] & PVTv2-B2 & ✓ & ✓ & 0.920 & 0.903 & 0.944 & 0.039 \\  & MFURNet [101] & ResNet-50 & ✗ & ✓ & 0.937 & 0.923 & - & 0.035 \\  & Group & ResNet-50 & ✗ & ✓ & 0.923 & 0.924 & 0.928 & 0.027 \\  & TransNet [102] & & ✓ & ✓ & 0.933 & 0.935 & 0.935 & 0.025 \\  & CPNet [23] & Swin-B & ✓ & ✓ & 0.931 & 0.936 & 0.939 & 0.025 \\  & HFIMNet [103] & Swin-Trans & ✓ & ✓ & 0.944 & 0.937 & 0.966 & 0.023 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._In contrast, models without an attention mechanism often experience a decline in accuracy when processing small objects or objects that blend into the background. The fixed feature processing strategies of these models fail to adapt to the varying prominence of salient features in complex scenes. As highlighted in the research by [PERSON] et al. [104], the application of self-attention has proven effective in mitigating these issues by enhancing feature representation and discriminability, significantly improving the capability of models to detect salient objects under diverse environmental conditions.