视觉Transformer的可解释性：综合评述与新视角
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives
AI总结：
本文综述了视觉Transformer的可解释性方法，提出按动机、结构和应用场景分类的分类法，并评述了评估标准、工具及未来研究方向，旨在理解模型决策并建立信任。
摘要：
Transformer已在自然语言处理领域产生重大影响，并最近展示了其在计算机视觉中的潜力。在基础计算机视觉任务中，它们已展现出优于卷积神经网络的结果。然而，科学界尚未完全理解视觉Transformer的内部工作机制，也未完全理解其决策依据，这凸显了可解释性方法的重要性。理解这些模型如何做出决策不仅能提升其性能，还能建立对AI系统的信任。本研究探讨了为视觉Transformer提出的不同可解释性方法，并提出了一个根据其动机、结构和应用场景进行组织的分类法。此外，它提供了对可用于比较解释结果的评估标准以及可解释性工具和框架的全面评述。最后，论文强调了可以增强视觉Transformer可解释性的重要但尚未探索的方面，并为未来的投资提出了有前景的研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
