Emotion Interaction Recognition Based on Deep Adversarial Network in Interactive Design for Intelligent Robot

Chen, Xiang; Xu, Lijun; Wu, Hua; Shang, Zhongan; Zhang, Tingyu; Zhang, Linghao

doi:10.1109/access.2019.2953882

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Predicting how art design will evolve and how intelligent elements will influence it is also important. Reference [28] compares the design of contemporary new media art with those of historic virtual art from the viewpoints of artistic aesthetics and creative expression. There is a comparison between conventional and virtual reality art in reference [29].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Zhao

2022

Journal of Sensors

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Interactive art design (IAD) is an organic integration of art and technology. From the perspective of the development of AI machines from ancient times to the present, it has gone through the stages of command interface, graphical interface, and multimedia interface. The development of interactive art has been around for some years. As an art form, it not only brings optimization and enjoyment to people’s quality of life but also meets the needs of human-computer interaction, improves the efficiency of art design, and realizes human-human interaction. The purpose is to bring different feelings and experiences to the works and people’s psychology. In particular, the application of AI in IAD has not only brought great changes to designers. It also derives the interaction of behavioral limbs, which brings a greater experience and interaction to the audience, thereby creating a better interactive effect. Therefore, this paper completes the following work: (1) the research progress of AI in IAD at home and abroad is introduced. (2) The combination of AI and IAD is proposed, and the basic principle of RBF neural network and intelligent optimization algorithm are introduced, and the evaluation index of IAD is constructed. (3) Using the constructed dataset to test two intelligent optimization algorithms, the results show that the PSO-RBF model is more excellent in evaluating the quality of IAD. The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small. Comparing the quality indicators of IAD before and after the integration of AI, the results show that AI has an excellent improvement effect on IAD.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Zhao

2022

Journal of Sensors

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“…Since its introduction in 2014, GAN [17] continues to attract growing interests in the deep learning community and has been applied to various domains such as computer vision [28]- [33], natural language processing [34], [35], time series synthesis [36], [37], and semantic segmentation [38], [39]. Specifically, GAN has shown significant recent success in the field of computer vision on a variety of tasks such as image generation [28], [29], image to image translation [30], [31], and image super-resolution [32], [33].…”

Section: A Generative Adversarial Networkmentioning

confidence: 99%

Attribute-Aware Generative Design With Generative Adversarial Networks

Yuan

Moghaddam

2020

IEEE Access

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The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process. The shrinking timeto-market and the growing diversity of users' needs further exacerbate this gap. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation or editing of design concepts. This paper explores the capabilities of generative adversarial networks (GAN) for automated, attribute-aware generative design of the visual attributes of a product. Specifically, a design attribute GAN (DA-GAN) model is developed for automated generation of fashion product images with the desired visual attributes. Experiments on a large fashion dataset signify the potentials of GAN for attribute-aware generative design, verify the ability of editing attributes with relatively higher accuracy and uncover several key challenges and research questions for future work.

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“…Various intelligent algorithms are often used in emotion analysis and other fields [ 16 – 18 ]. The core idea of used USVM is as follows: first, the original unbalanced data are divided into three areas: support vector area (SV), majority class nonsupport vector area (MNSV), and minority class nonsupport vector area (FNSV) according to the location.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology

Chen

Xiao

et al. 2022

Computational Intelligence and Neuroscience

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With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user’s experience of using digital museums has not been fully and deeply studied at present. In this study, the user’s experience evaluation data of digital museum are classified and processed, so as to analyze the user’s emotional trend towards the museum. Considering that the user’s evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.

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Emotion Interaction Recognition Based on Deep Adversarial Network in Interactive Design for Intelligent Robot

Cited by 10 publications

References 26 publications

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Attribute-Aware Generative Design With Generative Adversarial Networks

A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology

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