CreativeSeg: Semantic Segmentation of Creative Sketches

Yixiao Zheng;Kaiyue Pang;Ayan Das;Dongliang Chang;Yi-Zhe Song;Zhanyu Ma
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Abstract

The problem of sketch semantic segmentation is far from being solved. Despite existing methods exhibiting near-saturating performances on simple sketches with high recognisability, they suffer serious setbacks when the target sketches are products of an imaginative process with high degree of creativity. We hypothesise that human creativity, being highly individualistic, induces a significant shift in distribution of sketches, leading to poor model generalisation. Such hypothesis, backed by empirical evidences, opens the door for a solution that explicitly disentangles creativity while learning sketch representations. We materialise this by crafting a learnable creativity estimator that assigns a scalar score of creativity to each sketch. It follows that we introduce CreativeSeg, a learning-to-learn framework that leverages the estimator in order to learn creativity-agnostic representation, and eventually the downstream semantic segmentation task. We empirically verify the superiority of CreativeSeg on the recent “Creative Birds” and “Creative Creatures” creative sketch datasets. Through a human study, we further strengthen the case that the learned creativity score does indeed have a positive correlation with the subjective creativity of human. Codes are available at https://github.com/PRIS-CV/Sketch-CS .
CreativeSeg:创意草图的语义分割。
草图语义分割问题远未解决。尽管现有方法在具有高识别度的简单草图上表现出接近饱和的性能,但当目标草图是具有高度创造性的想象过程的产物时,这些方法就会遭受严重挫折。我们假设,人类的创造力是高度个性化的,会导致草图的分布发生显著变化,从而导致模型泛化效果不佳。有了经验证据的支持,这种假设为在学习草图表征时明确分离创造力的解决方案打开了大门。为此,我们设计了一种可学习的创造力估算器,为每个草图分配一个创造力标量分数。随后,我们介绍了 CreativeSeg,这是一个 "从学习到学习 "的框架,它利用估算器来学习与创意无关的表征,并最终完成下游的语义分割任务。我们在最近的 "创意鸟类 "和 "创意生物 "创意素描数据集上实证验证了 CreativeSeg 的优越性。通过对人类的研究,我们进一步证实了学习到的创造力得分确实与人类的主观创造力呈正相关。代码见 https://github.com/PRIS-CV/Sketch-CS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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