交互性×可解释性：探索交互性如何提升计算机视觉解释效果
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
AI总结：
针对计算机视觉静态解释存在的信息过载等问题，研究交互性在热图、概念、原型三类解释中的作用，通过鸟类识别任务研究发现交互性的优势与挑战，并提出设计建议。
摘要：
计算机视觉模型的解释是理解底层模型工作原理的重要工具，但它们常以静态形式呈现，给用户带来信息过载、语义与像素级信息脱节、探索机会有限等挑战。我们将交互性作为解决这些问题的机制，研究其在热图型、概念型和原型型三种常见解释类型中的应用。我们以鸟类识别任务开展研究（N=24），参与者技术与领域专长多样。结果显示，交互性虽增强用户控制、加速收敛至相关信息、助力用户扩展对模型及解释的理解，但也带来新挑战。为此，我们提出交互式计算机视觉解释的设计建议，包括精心选择默认视图、独立输入控件和受限输出空间。
英文摘要：
Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challenges for users, including information overload, a gap between semantic and pixel-level information, and limited opportunities for exploration. We investigate interactivity as a mechanism for tackling these issues in three common explanation types: heatmap-based, concept-based, and prototype-based explanations. We conducted a study (N=24), using a bird identification task, involving participants with diverse technical and domain expertise. We found that while interactivity enhances user control, facilitates rapid convergence to relevant information, and allows users to expand their understanding of the model and explanation, it also introduces new challenges. To address these, we provide design recommendations for interactive computer vision explanations, including carefully selected default views, independent input controls, and constrained output spaces.
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