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素描分割器:用于图形和创意素描的基于变压器的细分

Yixiao Zheng, Jiyang Xie, Aneeshan Sain

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|August 10, 2023

概括

本研究介绍了Sketch-Segformer,这是一个基于变压器的新型框架,用于草图语义细分. 它有效地利用多方面的草图信息来提高各种草图类型的准确性.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

草图的语义细分对于理解草图至关重要.
现有的方法往往侧重于草图数据的有限方面 (例如,整个图像,冲刺或序列).
需要在草图数据的多个方面探索互补信息.

研究的目的:

提出一个新的框架,Sketch-Segformer,用于草图语义细分.
为了证明整合多方面的草图信息的好处.
在传统和创意草图上实现最先进的性能.

主要方法:

开发了一个基于变压器的框架 (Sketch-Segformer),它将草图视为冲击序列.
引入了两个具有不同受感场的自我注意模块 (整个素描和单个冲刺).
集成的顺序,空间和冲动级嵌入.

主要成果:

在图形素描数据集 (SPG,SketchSeg-150K) 上实现了最先进的性能.
通过利用多方面的信息,在创意草图 (CreativeSketch数据集) 上表现出强的表现.
废除研究,可视化和不变性测试验证了设计选择.

结论:

拟议的Sketch-Segformer有效地利用了互补的多面素描信息.
该框架为各种素描类型的素描语义细分提供了一个强大的方法.
该研究强调了整体草图数据探索对于改进解释的重要性.

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