In this paper, we explore the unique modality of sketch for explainability,
emphasising the profound impact of human strokes compared to conventional
pixel-oriented studies. Beyond explanations of network behavior, we discern the
genuine implications of explainability across diverse downstream sketch-related
tasks. We propose a lightweight and portable explainability solution -- a
seamless plugin that integrates effortlessly with any pre-trained model,
eliminating the need for re-training. Demonstrating its adaptability, we
present four applications: highly studied retrieval and generation, and
completely novel assisted drawing and sketch adversarial attacks. The
centrepiece to our solution is a stroke-level attribution map that takes
different forms when linked with downstream tasks. By addressing the inherent
non-differentiability of rasterisation, we enable explanations at both coarse
stroke level (SLA) and partial stroke level (P-SLA), each with its advantages
for specific downstream tasks.

我们在该研究中探索了草图的独特模态来增加可解释性，强调人类笔画相对于传统面向像素的研究的深远影响。我们提出了一个轻量级且便携的可解释性解决方案 —— 一种无缝插件，可以与任何预训练模型无缝集成，并消除了重新训练的需求。我们通过展示四种应用程序来演示其适应性：高度研究的检索和生成，以及全新的辅助绘图和草图对抗攻击。我们的解决方案的核心是一个笔画级归因图，在与下游任务相连接时采用不同形式。通过解决栅格化的固有非可微性，我们实现了对粗略笔画级（SLA）和部分笔画级（P-SLA）的解释，每个级别都具有特定下游任务的优势。