# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods
### Summary of MDE Methods
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Models** & **Strategy** & **Methods** & **Attention Mechanism** & **Contributions** \\ \hline \multirow{9}{*}{Supervised} & ANUW [31], 2021 & CSA, SSA & U-net with channel and spatial attentions \\  & ACAN [32], 2021 & SSA & Context aggregation module \\  & BANet [33], 2021 & SCA & Bidirectional attention \\  & [PERSON] et al. [34], 2022 & CSA, SSA & Inserting attentions into skip connections \\  & [PERSON] et al. [35], 2022 & SSA & EdgeConv attention module \\  & MVA [37], 2022 & CSA, SSA & Multi-view attention model \\  & [PERSON] et al. [38], 2022 & CSA, SCA & Adaptive geometric attention \\  & PFN [39], 2022 & SSA & Spatial attention residual refinement \\  & [PERSON] et al. [40], 2022 & CCA & Temporal attention \\ \hline \multirow{9}{*}{CNN-based} & [PERSON] et al. [41], 2020 & CSA, SSA & Channel and spatial attentions \\  & [PERSON] et al. [42], 2021 & SSA & Spatial attention \\  & [PERSON] et al. [43], 2021 & SSA, CSA & Channel and spatial attentions \\  & [PERSON] et al. [44], 2020 & CSA, SSA & Channel and spatial attentions \\  & AgFU-Net [45], 2021 & CSA & Attention of skip connection \\  & [PERSON] et al. [46], 2020 & SSA & Prediction of discrete disparity with spatial attention \\  & TC-Depth [31], 2021 & SSA, SCA & Temporal attention \\ Self-supervised & CALDepth-Net [47], 2021 & CSA & Channel attention \\  & [PERSON] [48], 2021 & CSA & Channel attention at multi-scale \\  & MLDA-Net [50], 2021 & CSA & Channel attention using dot-product \\  & [PERSON] et al. [51], 2021 & SSA, SCA & Combing ASPP with spatial attention \\  & SHdétp [52], 2021 & SSA, CSA & Channel and spatial attentions \\  & [PERSON] et al. [53], 2022 & CSA, SSA & Normalized CBAM \\  & [PERSON] et al. [54], 2022 & SSA & 3D spatial attention \\  & EDNet [55], 2022 & CSA & Channel attention \\  & [PERSON] et al. [56], 2022 & CSA, SSA & Triaxial squeeze attention \\  & [PERSON] et al. [57], 2022 & CSA, SSA & Channel and spatial attentions \\  & VADepth [58], 2022 & CSA, SSA & Channel and spatial attentions \\ \hline \multirow{9}{*}{Transformer-based} & DPT [10], 2021 & SSA & Transformers as backbone \\  & [PERSON] et al. [59], 2022 & SSA & Transformers as backbone \\  & VUIT [15], 2022 & SSA & Transformers as backbone \\  & DPT-VO [60], 2022 & SSA & DPT for scale estimation \\  & RGFN [11], 2022 & SSA & Large kernel convolution attention \\  & BinsFormer [16], 2022 & SSA, SCA & Using Transformers to predict bins center \\  & DepthFormer [17], 2022 & SSA & Using Transformers to predict bins center \\  & Swin-Depth [63], 2021 & CSA, SSA & SW-Transformers as backbone \\  & PixelFormer [11], 2022 & CCA & Skip attention module \\  & RA-Swin [64], 2022 & SSA & SW-Transformers as backbone \\  & RT-ViT [65], 2022 & SSA & Lightweight framework \\  & SideRT [66], 2022 & SSA, SCA & Cross scale attention \\ \hline \multirow{9}{*}{Hybrid} & [PERSON] et al. [67], 2021 & SSA & Non-local fusion block \\  & & DEST [68], 2022 & SSA, SCA & Simplified Transformers for both depth and pose \\ \cline{1-1}  & Supervised & PPViT [69], 2022 & SSA & Spatial reduction before attention \\ \cline{1-1}  & TransDSSL [70], 2022 & SSA & Pixel-wise skip attention; Self-distillation loss \\ \cline{1-1}  & MT-S/MLEamer [71], 2022 & SSA & Estimate camera intrinsics \\ \hline \multirow{9}{*}{Hybrid} & \multirow{3}{*}{Supervised} & Adabins [73], 2020 & SSA & Predict pixel probability using attention \\ \cline{1-1}  & DPT-hybrid [10], 2021 & SSA & Hybrid encoder as backbone \\ \cline{1-1}  & PCTNet [75], 2022 & SSA & Use simple AdaBins module as decoder \\ \cline{1-1}  & DepthFormer [76], 2022 & SSA, SCA & Fuse features of Transformer and CNN \\ \cline{1-1}  & Focal-WNet [77], 2022 & SSA & Fuse features of Transformer and CNN \\ \cline{1-1}  & [PERSON] et al. [59], 2022 & SSA & Token attention \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of attention-based MDE methods from 2020 to 2022.