可解释性的火花：解释大型视觉模型的最新进展
Sparks of Explainability: Recent Advancements in Explaining Large Vision Models
AI总结：
本论文探索改进计算机视觉可解释性的方法，提出基于算法稳定性与Sobol指数的归因评估、EVA形式化保证方法，以及CRAFT概念提取与MACO可视化技术，构建统一框架揭示模型感知内容。
摘要：
本论文通过分析和建模深度神经网络所利用的特征，探索了改进计算机视觉可解释性的高级方法。首先，它评估了归因方法，特别是显著性图，通过引入基于算法稳定性的指标和利用Sobol指数的方法，后者通过准蒙特卡洛序列显著减少了计算时间。此外，EVA方法通过验证扰动分析提供了具有形式化保证的归因的首次表述。实验结果表明，在复杂场景中这些方法未能提供充分的理解，特别是因为它们仅识别模型关注“哪里”而未澄清它感知“什么”。因此检验了两个假设：通过引入结合模仿人类解释和在1-Lipschitz函数空间内优化的训练例程使模型与人类推理对齐，以及采用概念可解释性方法。提出了CRAFT方法以自动化提取模型使用的概念并评估其重要性，辅以MACO实现其可视化。这些工作汇聚成统一框架，通过应用于ResNet模型中1000个ImageNet类的交互式演示加以说明。
英文摘要：
This thesis explores advanced approaches to improve explainability in computer vision by analyzing and modeling the features exploited by deep neural networks. Initially, it evaluates attribution methods, notably saliency maps, by introducing a metric based on algorithmic stability and an approach utilizing Sobol indices, which, through quasi-Monte Carlo sequences, allows a significant reduction in computation time. In addition, the EVA method offers a first formulation of attribution with formal guarantees via verified perturbation analysis. Experimental results indicate that in complex scenarios these methods do not provide sufficient understanding, particularly because they identify only "where" the model focuses without clarifying "what" it perceives. Two hypotheses are therefore examined: aligning models with human reasoning -- through the introduction of a training routine that integrates the imitation of human explanations and optimization within the space of 1-Lipschitz functions -- and adopting a conceptual explainability approach. The CRAFT method is proposed to automate the extraction of the concepts used by the model and to assess their importance, complemented by MACO, which enables their visualization. These works converge towards a unified framework, illustrated by an interactive demonstration applied to the 1000 ImageNet classes in a ResNet model.
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。
