一种基于注意卷积神经网络的素描肖像智能评分方法

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Shaolong Zheng, Zewei Xu, Zhenni Li, Yihui Cai, Mingyu Han, Yi Ji
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引用次数: 0

摘要

对艺术专业的学生来说,得到及时的绘画反馈是非常重要的。目前,这项工作是由专业教师完成的。然而,由于人工评分的主观性和教师资源的稀缺,这种评分方法存在问题。在实践中进行这项工作既费时又昂贵。在本文中,我们提出了一种带有多头自注意模块的深度可分离卷积网络(DCMnet),用于开发素描肖像的智能评分机制。具体来说,为了构建轻量级网络,我们首先利用深度可分卷积块作为模型的主干来挖掘素描肖像的局部特征。然后使用注意力模块来注意肖像内部表示中的全局依赖关系。最后,我们使用DCMnet构建评分框架,首先将作品分为4个评分等级,再细分为60分以下、60-64分、65-69分、70-74分、75-79分、80-84分、85-89分、90分以上8个等级。每个等级的作品都有一个基本分数,作品的最终分数由基本分数和情绪因素组成。在训练过程中,引入了一种快速收敛的预训练策略。为了验证我们的方法,我们在广东美术联考中收集了一个素描肖像数据集来训练DCMnet。实验结果表明,该方法在每个等级上都达到了很好的准确率,提高了评分效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Scoring Method for Sketch Portrait Based on Attention Convolution Neural Network
It is very important for art students to get timely feedback on their paintings. Currently, this work is done by professional teachers. However, it is problematic for the scoring method since the subjectivity of manual scoring and the scarcity of teacher resources. It is time-consuming and expensive to carry out this work in practice. In this paper, we propose a depthwise separable convolutional network with multi-head self-attention module (DCMnet) for developing an intelligent scoring mechanism for sketch portraits. Specifically, to build a lightweight network, we first utilize the depthwise separable convolutional block as the backbone of the model for mining the local features of sketch portraits. Then the attention module is employed to notice global dependencies within internal representations of portraits. Finally, we use DCMnet to build a scoring framework, which first divides the works into four score levels, and then subdivides them into eight grades: below 60, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, and above 90. Each grade of work is given a basic score, and the final score of works is composed of the basic score and the mood factor. In the training process, a pretraining strategy is introduced for fast convergence. For verifying our method, we collect a sketch portrait dataset in the Guangdong Fine Arts Joint Examination to train the DCMnet. The experimental results demonstrate that the proposed method achieves excellent accuracy at each grade and the efficiency of scoring is improved.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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