  Transformers have had a significant impact on natural language processing and
have recently demonstrated their potential in computer vision. They have shown
promising results over convolution neural networks in fundamental computer
vision tasks. However, the scientific community has not fully grasped the inner
workings of vision transformers, nor the basis for their decision-making, which
underscores the importance of explainability methods. Understanding how these
models arrive at their decisions not only improves their performance but also
builds trust in AI systems. This study explores different explainability
methods proposed for visual transformers and presents a taxonomy for organizing
them according to their motivations, structures, and application scenarios. In
addition, it provides a comprehensive review of evaluation criteria that can be
used for comparing explanation results, as well as explainability tools and
frameworks. Finally, the paper highlights essential but unexplored aspects that
can enhance the explainability of visual transformers, and promising research
directions are suggested for future investment.
