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English(EN) SketchMamba: A Lightweight State-Space Model for Joint Progressive Sketch Classification and Stroke Auto-Completion

SketchMamba模型统一草图分类和生成

研究人员开发了SketchMamba,这是一种新颖的轻量级状态空间模型,专为实时草图分类和笔画自动补全而设计。与以往分别处理识别和生成问题的模型不同,SketchMamba将草图视为单一因果序列进行处理,使其能够对正在绘制的图形进行渐进式分类,并同时预测其续画。在Quick, Draw!数据集的一个子集上进行测试,SketchMamba在最后一步达到了94.93%的准确率,并展现出0.706的强劲渐进式准确率AUC,在仅绘制了70%的笔画时就达到了最终准确率的90%以上。该模型的有效性归功于其选择性状态空间骨干网络上应用的密集逐步分类损失,在匹配预算的比较中优于Transformer、循环和卷积基线模型。 AI

影响 该模型可以通过实现实时分类和生成,从而支持更直观、响应更快的绘图应用程序。

排序理由 这是一篇研究论文,详细介绍了新的模型架构及其在特定任务上的性能。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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SketchMamba模型统一草图分类和生成

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这是一篇研究论文,详细介绍了新的模型架构及其在特定任务上的性能。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Kavish Jhaveri, Arya Shah ·

    SketchMamba:一种用于联合渐进式草图分类和笔画自动补全的轻量级状态空间模型

    arXiv:2607.23580v1 Announce Type: new Abstract: Existing vector-sketch models treat recognition and generation as separate tasks, leaving a gap for streaming interfaces that must understand a drawing as it is being made. We present SketchMamba, a single causal sequence model that…