DEX-AR: 一种用于自回归视觉-语言模型的动态可解释性方法
DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models
AI总结：
DEX-AR是一种针对自回归视觉-语言模型的动态可解释性方法，通过生成2D热图来解释模型决策过程，提升模型可解释性和性能评估。
摘要：
随着视觉-语言模型（VLMs）变得越来越复杂和广泛使用，理解其决策过程变得越来越重要。传统的可解释性方法，设计用于分类任务，由于现代自回归VLMs的复杂逐token生成过程和视觉与文本模态之间的复杂交互，难以应对。我们提出了DEX-AR（动态可解释性用于自回归模型），一种新的可解释性方法，通过生成每个token和序列级别的2D热图，突出对模型文本响应至关重要的图像区域，以解决这些挑战。该方法通过在逐token生成过程中计算注意力图的层间梯度来解释自回归VLMs，包括不同层的重要性和生成token。DEX-AR引入了两个关键创新：一种动态头部过滤机制，用于识别关注视觉信息的注意力头，以及一种序列级过滤方法，用于聚合每个token的解释并区分视觉基础和纯语言token。我们在ImageNet、VQAv2和PascalVOC上的评估显示，在基于扰动的度量标准（使用新的标准化perplexity度量）以及基于分割的度量标准上均实现了持续的改进。
英文摘要：
As Vision-Language Models (VLMs) become increasingly sophisticated and widely used, it becomes more and more crucial to understand their decision-making process. Traditional explainability methods, designed for classification tasks, struggle with modern autoregressive VLMs due to their complex token-by-token generation process and intricate interactions between visual and textual modalities. We present DEX-AR (Dynamic Explainability for AutoRegressive models), a novel explainability method designed to address these challenges by generating both per-token and sequence-level 2D heatmaps highlighting image regions crucial for the model's textual responses. The proposed method offers to interpret autoregressive VLMs-including varying importance of layers and generated tokens-by computing layer-wise gradients with respect to attention maps during the token-by-token generation process. DEX-AR introduces two key innovations: a dynamic head filtering mechanism that identifies attention heads focused on visual information, and a sequence-level filtering approach that aggregates per-token explanations while distinguishing between visually-grounded and purely linguistic tokens. Our evaluation on ImageNet, VQAv2, and PascalVOC, shows a consistent improvement in both perturbation-based metrics, using a novel normalized perplexity measure, as well as segmentation-based metrics.
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。
