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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83883
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DC 欄位值語言
dc.contributor.advisor李瑞庭(Anthony J. T. Lee)
dc.contributor.authorMing-Min Hsuen
dc.contributor.author許明敏zh_TW
dc.date.accessioned2023-03-19T21:22:00Z-
dc.date.copyright2022-07-22
dc.date.issued2022
dc.date.submitted2022-07-19
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IEEE Transactions on Visualization and Computer Graphics 27, 10 (October 2021), 4039–4048. [19] Ji?? Vysko?il and Luk?? Picek. 2021. Improving web user interface element detection using Faster R-CNN. In Proceedings of the Conference and Labs of the Evaluation Forum, 1375–1386. [20] Kai Wang, Manolis Savva, Angel X. Chang, and Daniel Ritchie. 2018. Deep convolutional priors for indoor scene synthesis. ACM Transactions on Graphics 37, 4 (August 2018), 1–14. [21] Kamal Gupta, Justin Lazarow, Alessandro Achille, Larry S Davis, Vijay Mahadevan, and Abhinav Shrivastava. 2021. LayoutTransformer: Layout generation and completion with self-attention. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 1004–1014. [22] Karan Girotra, Christian Terwiesch, and Karl T. Ulrich. 2010. Idea generation and the quality of the best idea. Management Science 56, 4 (April 2010), 591–605. [23] Laura J. Kornish and Karl T. Ulrich. 2011. Opportunity spaces in innovation: Empirical analysis of large samples of ideas. Management Science 57, 1 (January 2011), 107–128. [24] Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In Proceedings of European Semantic Web Conference, 593–607. [25] Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, and Furu Wei. 2021. TrOCR: Transformer-based optical character recognition with pre-trained models. arXiv:2109.10282 (September 2021). [26] Nelson Nauata, Kai-Hung Chang, Chin-Yi Cheng, Greg Mori, and Yasutaka Furukawa. 2020. House-GAN: Relational generative adversarial networks for graph-constrained house layout generation. In Proceedings of the European Conference on Computer Vision, 162–177. [27] Patti Bao, Elizabeth Gerber, Darren Gergle, and David Hoffman. 2010. Momentum: Getting and staying on topic during a brainstorm. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1233–1236. [28] Peter O’Donovan, Aseem Agarwala, and Aaron Hertzmann. 2015. Designscape: Design with interactive layout suggestions. In Proceedings of the Annual ACM Conference on Human Factors in Computing Systems, 1221–1224. [29] Qi Chen, Qi Wu, Rui Tang, Yuhan Wang, Shuai Wang, and Mingkui Tan. 2020. Intelligent home 3D: Automatic 3D-house design from linguistic descriptions only. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12625–12634. [30] Ralph L. Keeney. 2012. Value-focused brainstorming. Decision Analysis 9, 4 (December 2012), 303–313. [31] Remko Van der Lugt. 2005. How sketching can affect the idea generation process in design group meetings. Design Studies 26, 2 (March 2005), 101–122. [32] Rex Hartson and Pardha Pyla. 2018. The UX Book: Agile UX Design for a Quality User Experience. Morgan Kaufmann, San Francisco. [33] Saul Greenberg, Sheelagh Carpendale, Nicolai Marquardt, and Bill Buxton. 2012. Sketching User Experiences: The Workbook. Morgan Kaufmann, Boston. [34] Shao-Kui Zhang, Wei-Yu Xie, and Song-Hai Zhang. 2021. Geometry-based layout generation with hyper-relations among objects. Graphical Models 116, (July 2021), 101104. [35] Stylianos Kavadias and Svenja C. Sommer. 2009. The effects of problem structure and team diversity on brainstorming effectiveness. Management Science 55, 12 (December 2009), 1899–1913. [36] Thomas F. Liu, Mark Craft, Jason Situ, Ersin Yumer, Radomir Mech, and Ranjitha Kumar. 2018. Learning design semantics for mobile apps. In Proceedings of the Annual ACM Symposium on User Interface Software and Technology, 569–579. [37] Tianming Zhao, Chunyang Chen, Yuanning Liu, and Xiaodong Zhu. 2021. GUIGAN: Learning to generate GUI designs using generative adversarial networks. In Proceedings of the IEEE/ACM International Conference on Software Engineering, 748–760. [38] Valentina Lenarduzzi and Davide Taibi. 2016. MVP explained: A systematic mapping study on the definitions of minimal viable product. In Proceedings of the IEEE Euromicro Conference on Software Engineering and Advanced Applications, 112–119. [39] Xinru Zheng, Xiaotian Qiao, Ying Cao, and Rynson WH Lau. 2019. Content-aware generative modeling of graphic design layouts. ACM Transactions on Graphics 38, 4 (2019), 1–15. [40] Yingwei Pan, Zhaofan Qiu, Ting Yao, Houqiang Li, and Tao Mei. 2017. To create what you tell: Generating videos from captions. In Proceedings of the 25th ACM international conference on Multimedia, 1789–1798. [41] Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. 2019. StoryGAN: A sequential conditional GAN for story visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6322–6331. [42] Yue Jiang, Ruofei Du, Christof Lutteroth, and Wolfgang Stuerzlinger. 2019. ORC layout: Adaptive GUI layout with OR-constraints. In Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–12.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83883-
dc.description.abstract為了因應快速變遷的趨勢與增加顧客契合度,企業常使用腦力激盪開發創新點子、尋找解決方案與擬定策略決策,以滿足客戶需求。當進行腦力激盪發想時,企業常利用一序列的手繪草稿圖傳遞想法的概念與流程,將這些手繪草稿圖轉換成佈局線框圖,可以增進彼此的溝通,建立共識,並進一步描繪產品設計和實作的細節。但據我們所知,目前未有研究專注於從手繪草稿圖序列中生成線框佈局圖序列,因此,我們提出了一個研究架構從一系列手繪草稿圖中生成線框佈局圖。我們提出的研究架構包含四個階段:第一階段,我們偵測草稿圖序列中每個草稿圖上的元件,並得出元件之間的空間和方向關係;第二階段,我們運用關係圖卷積網路,並利用元件間空間和方向的關係聚合節點特徵;第三階段,我們設計了一個線框佈局模型,並使用狀態、內部草圖和跨草圖三種注意力機制來生成每個草稿圖的佈局;最後,我們開發了一個渲染模組,將生成的佈局、辨識出的文本內容與各種元件渲染成線框佈局圖序列。實驗結果顯示,我們所提出的研究架構優於比較方法,且生成出來的線框佈局圖不僅可準確傳達空間和方向對齊的資訊,亦可產生接近實際應用範例的佈局。我們的研究架構可幫助企業,在腦力激盪的過程中,將他們的想法從手繪草稿圖轉換為線框佈局圖,並及時且具有成本效益地加快設計流程,進而幫助企業快速反應市場需求,增進其市場競爭力。zh_TW
dc.description.abstractTo catch fast-moving trends and increase customer engagement, businesses often employ the brainstorming technique to develop innovative ideas, find appropriate solutions to problems, and make strategic decisions for satisfying the needs of customers. The outcome of brainstorming or idea generation may be often presented by a sequence of sketches for showing a concept or process flow. Deriving the layout wireframes from those sketches may bridge the gap in communicating with others during brainstorming sessions and depict the concept or process flow for further design and implementation. To the best of our knowledge, there is no study dedicated to generating the layouts from a sequence of sketches. Therefore, we propose a framework to generate the wireframes from a sequence of sketches. Our proposed framework contains four phases. First, we detect the components in each sketch of the input sequence and derive the spatial and directional relationships between components. Second, we utilize the relational graph convolutional networks to aggregate the node features through the derived spatial and directional relationships. Third, we devise a wireframe layout model to generate the layout of each sketch by using three attention mechanisms namely, state, inner-sketch, and across-sketch. Finally, we develop a rendering module to generate the wireframes for the input sketch sequence. The experimental results show that our proposed framework outperforms the comparing methods, and can generate the layout wireframes not only accurately convey the information of the spatial and directional alignment but are also close to the real applications. The proposed framework may help businesses convert their ideas from sketches to wireframes during brainstorming and speed up their design processes in a timely and cost-efficient manner, which in turn help business catch up with the opportunities in the market.en
dc.description.provenanceMade available in DSpace on 2023-03-19T21:22:00Z (GMT). No. of bitstreams: 1
U0001-1607202210354600.pdf: 3360509 bytes, checksum: 484bfd020e600fbc99c1650ded8ac882 (MD5)
Previous issue date: 2022
en
dc.description.tableofcontentsTable of Contents i List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Brainstorming and Idea Generation 5 2.2 Layout 6 Chapter 3 The Proposed Framework 8 3.1 Components and Text Contents Detection 8 3.2 Relational Graph Convolutional Network 12 3.3 Wireframe Layout Model 14 3.3.1 Encoder 14 3.3.2 Decoder 15 3.4 Rendering Module 17 Chapter 4 Experimental Results 20 4.1 Dataset and Experimental Setup 20 4.2 Performance Evaluation 22 4.2.1 Automatic Evaluation 23 4.2.2 Human Evaluation 28 4.3 Generated Examples 31 Chapter 5 Conclusions and Future Work 38 References 41
dc.language.isoen
dc.subject關係圖卷積網路zh_TW
dc.subject腦力激盪zh_TW
dc.subject產品設計與開發zh_TW
dc.subject佈局生成zh_TW
dc.subject深度學習zh_TW
dc.subjectbrainstormingen
dc.subjectdeep learningen
dc.subjectlayout generationen
dc.subjectproduct design and developmenten
dc.subjectrelational graph convolutional networken
dc.title描繪腦力激盪草圖之深度學習模型zh_TW
dc.titleA Deep Learning Framework of Sketching Brainstormsen
dc.typeThesis
dc.date.schoolyear110-2
dc.description.degree碩士
dc.contributor.advisor-orcid李瑞庭(0000-0003-0320-7309)
dc.contributor.oralexamcommittee許秉瑜(Ping-Yu Hsu),劉敦仁(Duen-Ren Liu)
dc.subject.keyword腦力激盪,產品設計與開發,佈局生成,深度學習,關係圖卷積網路,zh_TW
dc.subject.keywordbrainstorming,product design and development,layout generation,deep learning,relational graph convolutional network,en
dc.relation.page45
dc.identifier.doi10.6342/NTU202201496
dc.rights.note未授權
dc.date.accepted2022-07-19
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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