重新思考显著性图：一种与人类认知对齐的分类和评估框架用于解释
Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
AI总结：
本文提出RFxG分类法，通过区分点对点与对比性解释及不同粒度，改进显著性图的评估框架，以更符合人类认知需求。
摘要：
显著性图在深度学习中的视觉解释中被广泛使用，但关于其预期目的和与多样化用户查询的对齐仍然缺乏基本共识。这种模糊性阻碍了解释方法的有效评估和实际应用。我们通过引入参考框架×粒度（RFxG）分类法来填补这一空白，这是一种原则性的概念框架，沿着两个关键轴线组织显著性解释：参考框架：区分点对点（“为什么这个预测？”）和对比性（“为什么这个而不是替代？”）解释。粒度：从细粒度的类别级别（例如，“为什么哈士奇？”）到粗粒度的群体级别（例如，“为什么狗？”）的解释。通过RFxG的视角，我们展示了现有评估指标的关键局限性，这些指标过度优先考虑点对点的忠实度，而忽视了对比推理和语义粒度。为了系统地评估解释质量在两个RFxG维度上，我们提出了四个新的忠实度指标。我们的综合评估框架将这些指标应用于十种最先进的显著性方法、四种模型架构和三个数据集。通过倡导向用户意图驱动评估的转变，我们的工作提供了概念基础和实用工具，以开发不仅忠实于底层模型行为，而且与人类理解和探索的复杂性有意义对齐的视觉解释。
英文摘要：
Saliency maps are widely used for visual explanations in deep learning, but a fundamental lack of consensus persists regarding their intended purpose and alignment with diverse user queries. This ambiguity hinders the effective evaluation and practical utility of explanation methods. We address this gap by introducing the Reference-Frame $\times$ Granularity (RFxG) taxonomy, a principled conceptual framework that organizes saliency explanations along two essential axes:Reference-Frame: Distinguishing between pointwise ("Why this prediction?") and contrastive ("Why this and not an alternative?") explanations. Granularity: Ranging from fine-grained class-level (e.g., "Why Husky?") to coarse-grained group-level (e.g., "Why Dog?") interpretations. Using the RFxG lens, we demonstrate critical limitations in existing evaluation metrics, which overwhelmingly prioritize pointwise faithfulness while neglecting contrastive reasoning and semantic granularity. To systematically assess explanation quality across both RFxG dimensions, we propose four novel faithfulness metrics. Our comprehensive evaluation framework applies these metrics to ten state-of-the-art saliency methods, four model architectures, and three datasets. By advocating a shift toward user-intent-driven evaluation, our work provides both the conceptual foundation and the practical tools necessary to develop visual explanations that are not only faithful to the underlying model behavior but are also meaningfully aligned with the complexity of human understanding and inquiry.
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。
