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\begin{table*}
\centering
\caption{Summary of popular Vision Transformer Survey publications}
\label{tab:Survey}
\textbf{Author~ ~ ~ ~~} \& \textbf{Focus} \& \textbf{Contributions} \& \textbf{Descriptions} \& \textbf{Publisher}\\
Khan et al. \cite{khan2022transformers} \& {Overall survey of X-former applications in vision tasks} \& {Comprehensive, covers all major application areas, including performance comparisons} \& {Analyzes open challenges and future directions of ViT} \& ACM Computing Surveys'21\\
Han et al. \cite{han2022survey} \& {Overview of Transformer architectures~for computer vision tasks} \& {Strong emphasis on model design and variants, presents theoretical comparisons} \& Taxonomy of X-formers based on architectural design \& IEEE PAMI'20\\
Liu et al. \cite{liu2023survey} \& Wide range of visual Transformer applications beyond recognition tasks \& {Detailed discussion of multi-modal tasks, video processing, and 3D analysis} \& {Highlights the potential of Visual X-formers for low-level vision and generative tasks} \& IEEE NNLS'21\\
Xu et al. \cite{xu2022transformers} ~ \& {Focus on visual media such as images, videos and point clouds} \& {Emphasizes temporal modelling and 3D data processing} \& {Analyzes applications in areas such as video analysis, motion prediction, etc.} \& {Springer CVM'21}\\
Tay et al.\cite{Tay2020EfficientTA}~ \& Overview of techniques for improving Transformer efficiency \& {Provides a comprehensive overview of existing work and models across multiple domains} \& Help researchers to navigate in the selection of recent efficiency-flavored “X-former” models \& ACM Computing Surveys'20\\
Kameswari et al. \cite{kameswari2023overview} \& Specific focus on image processing applications of Transformers \& {Compares performance of Transformers with traditional CNNs} \& {Introduces hybrid architectures combining Transformers and CNNs for image processing tasks} \& IJACSA'23\\
Shehzadi et al. \cite{shehzadi20232d} \& Focused review of X-former based object detection \& {Covers various Transformer architectures for object detection.} \& Provides insights into integrating Transformers with existing object detection frameworks \& arXiv'23\\
\textbf{Our} \& {Focused on popular X-formers and review of ViTs for different vision domains} \& Compares various NLP and Vision Transformer including theory, analyze limitations, and future directions~ \& {Simple introduction of X-formers and ViTs, highlights the potential of ViTs in different CV tasks} \& NA
\end{table*}