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Sketch- inspector:用于生成高质量猫咪草图的深度混合模型(CS CV)

2020-12-07 19:23:47 凌茜

在人工智能(AI)的参与下,可以在特定主题下自动生成草图。尽管之前的研究在这方面取得了突破,但相对较高的比例生成的图形过于抽象,无法识别,这说明AIs在绘制时无法学习目标物体的大致图案。本文认为,对笔画生成过程的监控可以使素描的理解更加准确。在此基础上,本文提出了一种基于卷积神经网络预测器的素描生成系统,用于提示下一笔画的形状。此外,提出了一种基于cnn的鉴别器来判断最终产品的可识别性。因为基线模型在生成多类草图方面是无效的,所以我们限制模型只能生成一个类别。由于猫的图像很容易识别,我们考虑从QuickDraw数据集中选择猫的草图。本文将提出的模型与原始草图在75000人画猫草图上进行比较。结果表明,该模型生成的草图质量优于人类的草图。

原文题目:Sketch-Inspector: a Deep Mixture Model for High-Quality Sketch Generation of Cats

原文:With the involvement of artificial intelligence (AI), sketches can be automatically generated under certain topics. Even though breakthroughs have been made in previous studies in this area, a relatively high proportion of the generated figures are too abstract to recognize, which illustrates that AIs fail to learn the general pattern of the target object when drawing. This paper posits that supervising the process of stroke generation can lead to a more accurate sketch interpretation. Based on that, a sketch generating system with an assistant convolutional neural network (CNN) predictor to suggest the shape of the next stroke is presented in this paper. In addition, a CNN-based discriminator is introduced to judge the recognizability of the end product. Since the base-line model is ineffective at generating multi-class sketches, we restrict the model to produce one category. Because the image of a cat is easy to identify, we consider cat sketches selected from the QuickDraw data set. This paper compares the proposed model with the original Sketch-RNN on 75K human-drawn cat sketches. The result indicates that our model produces sketches with higher quality than human's sketches.

原文作者:Yunkui Pang, Zhiqing Pan, Ruiyang Sun, Shuchong Wang

原文地址:https://arxiv.org/abs/2011.04280

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https://cloud.tencent.com/developer/article/1748740