What Sketch Explainability Really Means for Downstream Tasks
<|reference_start|>What Sketch Explainability Really Means for Downstream Tasks: 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.<|reference_end|>
@article{bandyopadhyay2024what,
    title={What Sketch Explainability Really Means for Downstream Tasks},
    author={Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia,
  Aneeshan Sain, Tao Xiang, Yi-Zhe Song},
    journal={arXiv preprint arXiv:2403.09480},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09480},
    primaryClass={cs.CV cs.AI}
}