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What Sketch Explainability Really Means for Downstream Tasks?

Hmrishav Bandyopadhyay · Pinaki Nath Chowdhury · Ayan Kumar Bhunia · Aneeshan Sain · Tao Xiang · Yi-Zhe Song

Arch 4A-E Poster #131
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


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.

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