{"type":"ScholarlyArticle","title":"Learning Object Consistency and Interaction in Image Generation from Scene Graphs","authors":[{"name":"Yangkang Zhang","slug":"yangkang-zhang","given":"Yangkang","family":"Zhang","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Chenye Meng","slug":"chenye-meng","given":"Chenye","family":"Meng","affiliation":"School of Software Technology , Zhejiang University , China"},{"name":"Zejian Li","slug":"zejian-li","given":"Zejian","family":"Li","affiliation":"School of Software Technology , Zhejiang University , China"},{"name":"Pei Chen","slug":"pei-chen","given":"Pei","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Guang Yang","slug":"guang-yang","given":"Guang","family":"Yang","affiliation":"Alibaba Group"},{"name":"Changyuan Yang","slug":"changyuan-yang","given":"Changyuan","family":"Yang","affiliation":"Alibaba Group"},{"name":"Lingyun Sun","slug":"lingyun-sun","given":"Lingyun","family":"Sun","affiliation":"College of Computer Science and Technology , Zhejiang University , China"}],"abstract":"This paper is concerned with synthesizing images conditioned on a scene graph (SG), a set of object nodes and their edges of interactive relations. We divide existing works into image-oriented and code-oriented methods. In our analysis, the image-oriented methods do not consider object interaction in spatial hidden feature. On the other hand, in empirical study, the code-oriented methods lose object consistency as their generated images miss certain objects in the input scene graph. To alleviate these two issues, we propose Learning Object Consistency and Interaction (LOCI). To preserve object consistency, we design a consistency module with a weighted augmentation strategy for objects easy to be ignored and a matching loss between scene graphs and image codes. To learn object interaction, we design an interaction module consisting of three kinds of message propagation between the input scene graph and the learned image code. Experiments on COCO-stuff and Visual Genome datasets show our proposed method alleviates the ignorance of objects and outperforms the state-of-the-art on visual fidelity of generated images and objects.","doi":"10.24963/ijcai.2023/192","url":"https://ijcai.org/proceedings/2023/192","pdf":"https://ijcai.org/proceedings/2023/0192.pdf","markdown":"https://ijcai.org/proceedings/2023/192.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/192","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":192,"pages":{"first":1731,"last":1739},"keywords":["Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Neural generative models, auto encoders, GANs"],"videos":[],"references":[{"position":1,"raw":"[Ashual and Wolf, 2019] Oron Ashual and Lior Wolf. Spec- ifying object attributes and relations in interactive scene generation. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4560-4568, 2019.","title":"Specifying object attributes and relations in interactive scene generation","authors":"Wolf ; Ashual, Oron Ashual, Lior Wolf","year":"2019","venue":"IEEE/CVF International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ba et al., 2016] Jimmy Lei Ba, Jamie Ryan Kiros, and Ge- offrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.","title":"Layer normalization","authors":"Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E Hinton","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Caesar et al., 2018] Holger Caesar, Jasper Uijlings, and Vit- torio Ferrari. COCO-stuff: Thing and stuff classes in con- text. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1209-1218, 2018.","title":"COCO-stuff: Thing and stuff classes in context","authors":"Holger Caesar, Jasper Uijlings, Vittorio Ferrari","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dhamo et al., 2020] Helisa Dhamo, Azade Farshad, Iro Laina, Nassir Navab, Gregory Hager, Federico Tombari, and Christian Rupprecht. Semantic image manipulation using scene graphs. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5212- 5221, 2020.","title":"Semantic image manipulation using scene graphs","authors":"Dhamo","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Esser et al., 2021] Patrick Esser, Robin Rombach, and Björn Ommer. Taming transformers for high-resolution image synthesis. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12868-12878, 2021.","title":"Taming transformers for high-resolution image synthesis","authors":"Patrick Esser, Robin Rombach, Björn Ommer","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fan et al., 2022] Wanshu Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, and Yu-Chiang Frank Wang. Frido: Feature pyramid diffusion for complex scene image synthesis. ArXiv, abs/2208.13753, 2022.","title":"Frido: Feature pyramid diffusion for complex scene image synthesis","authors":"Wanshu Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank, Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Han et al., 2022] Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu. Vision GNN: An image is worth graph of nodes. arXiv preprint arXiv:2206.00272, 2022.","title":"Vision GNN: An image is worth graph of nodes","authors":"Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, Enhua Wu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2020] Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, and Yuan-Fang Li. Learning from the scene and bor- rowing from the rich: Tackling the long tail in scene graph generation. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Arti- ficial Intelligence, IJCAI-20, pages 587-593. International Joint Conferences on Artificial Intelligence Organization, 7 2020.","title":"Learning from the scene and borrowing from the rich: Tackling the long tail in scene graph generation","authors":"Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, Yuan-Fang Li","year":"2020","venue":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20","doi":null,"url":null,"internal_url":"/proceedings/2020/82"},{"position":9,"raw":"[Hendrycks and Gimpel, 2016] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415, 2016.","title":"Gaussian error linear units (GELUs)","authors":"Gimpel Hendrycks, Dan Hendrycks, Kevin Gimpel","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Herzig et al., 2020] Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, and Amir Globerson. Learn- ing canonical representations for scene graph to image generation. In European Conference on Computer Vision, pages 210-227. Springer, 2020.","title":"Learning canonical representations for scene graph to image generation","authors":"Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Heusel et al., 2017] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochre- iter. GANs trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30:6626-6637, 2017.","title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","authors":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hua et al., 2021] Tianyu Hua, Hongdong Zheng, Yalong Bai, Wei Zhang, Xiao-Ping Zhang, and Tao Mei. Exploit- ing relationship for complex-scene image generation. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 35, pages 1584-1592, 2021.","title":"Exploiting relationship for complex-scene image generation","authors":"Tianyu Hua, Hongdong Zheng, Yalong Bai, Wei Zhang, Xiao-Ping Zhang, Tao Mei","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ivgi et al., 2021] Maor Ivgi, Yaniv Benny, Avichai Ben- David, Jonathan Berant, and Lior Wolf. Scene graph to image generation with contextualized object layout refine- ment. In 2021 IEEE International Conference on Image Processing (ICIP), pages 2428-2432. IEEE, 2021.","title":"Scene graph to image generation with contextualized object layout refinement","authors":"Maor Ivgi, Yaniv Benny, Avichai Ben-David, Jonathan Berant, Lior Wolf","year":"2021","venue":"IEEE International Conference on Image Processing (ICIP)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jahn et al., 2021] Manuel Jahn, Robin Rombach, and Björn Ommer. High-resolution complex scene synthesis with transformers. In IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition Workshop (CVPRW), 2021.","title":"High-resolution complex scene synthesis with transformers","authors":"Manuel Jahn, Robin Rombach, Björn Ommer","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Johnson et al., 2018] Justin Johnson, Agrim Gupta, and Li Fei-Fei. Image generation from scene graphs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1219-1228, 2018.","title":"Image generation from scene graphs","authors":"Justin Johnson, Agrim Gupta, Li Fei-Fei","year":"2018","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Krishna et al., 2017] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. Inter- national Journal of Computer Vision, 123(1):32-73, 2017.","title":"Visual genome: Connecting language and vision using crowdsourced dense image annotations","authors":"Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma","year":"2017","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2019] Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, and Xiaogang Wang. PasteGAN: A semi- parametric method to generate image from scene graph. Advances in Neural Information Processing Systems, 32:3950-3960, 2019.","title":"PasteGAN: A semiparametric method to generate image from scene graph","authors":"Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2021] Zejian Li, Jingyu Wu, Immanuel Koh, Yongchuan Tang, and Lingyun Sun. Image synthesis from layout with locality-aware mask adaption. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13819-13828, 2021.","title":"Image synthesis from layout with locality-aware mask adaption","authors":"Li","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Luo et al., 2021] Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, and Xi Wu. Stochas- tic actor-executor-critic for image-to-image translation. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth In- ternational Joint Conference on Artificial Intelligence, IJCAI-21, pages 2775-2781. International Joint Confer- ences on Artificial Intelligence Organization, 8 2021.","title":"Stochastic actor-executor-critic for image-to-image translation","authors":"Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, Xi Wu","year":"2021","venue":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21","doi":null,"url":null,"internal_url":"/proceedings/2021/382"},{"position":20,"raw":"[Park et al., 2019] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive normalization. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2332-2341, 2019.","title":"Semantic image synthesis with spatially-adaptive normalization","authors":"Park","year":"2019","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Radford et al., 2019] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. https://cdn.openai.com/better- language-models/language models are unsupervised multitask learners.pdf, last accessed on 18th Jan, 2023.","title":"Language models are unsupervised multitask learners","authors":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever","year":"2019","venue":"OpenAI blog","doi":null,"url":"https://cdn.openai.com/better-language-models/languagemodelsareunsupervisedmultitasklearners.pdf","internal_url":null},{"position":22,"raw":"[Ramesh et al., 2021] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821-8831. PMLR, 2021.","title":"Zero-shot text-to-image generation","authors":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Salimans et al., 2016] Tim Salimans, Ian Goodfellow, Woj- ciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training GANs. Advances in Neu- ral Information Processing Systems, 29:2226-2234, 2016.","title":"Improved techniques for training GANs","authors":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Schaldenbrand et al., 2022] Peter Schaldenbrand, Zhixuan Liu, and Jean Oh. StyleCLIPDraw: Coupling content and style in text-to-drawing translation. In Lud De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pages 4966-4972. International Joint Conferences on Artificial Intelligence Organization, 7 2022.","title":"StyleCLIPDraw: Coupling content and style in text-to-drawing translation","authors":"Peter Schaldenbrand, Zhixuan Liu, Jean Oh","year":"2022","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22","doi":null,"url":null,"internal_url":"/proceedings/2022/688"},{"position":25,"raw":"[Sylvain et al., 2021] Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R Devon Hjelm, and Shikhar Sharma. Object-centric image generation from layouts. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pages 2647-2655, 2021.","title":"Object-centric image generation from layouts","authors":"Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, Devon Hjelm, Shikhar Sharma","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tseng et al., 2020] Hung-Yu Tseng, Hsin-Ying Lee, Lu Jiang, Ming-Hsuan Yang, and Weilong Yang. Re- trieveGAN: Image synthesis via differentiable patch retrieval. In European Conference on Computer Vision, pages 242-257. Springer, 2020.","title":"Re-trieveGAN: Image synthesis via differentiable patch retrieval","authors":"Hung-Yu Tseng, Hsin-Ying Lee, Lu Jiang, Ming-Hsuan Yang, Weilong Yang","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Sys- tems, 30:5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Velickovic et al., 2018] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In Interna- tional Conference on Learning Representations (ICLR), 2018.","title":"Graph attention networks","authors":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio","year":"2018","venue":"International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2022] Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of- freebies sets new state-of-the-art for real-time object de- tectors. arXiv preprint arXiv:2207.02696, 2022.","title":"YOLOv7: Trainable bag-offreebies sets new state-of-the-art for real-time object detectors","authors":"Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Xu and Xu, 2022] Xiaogang Xu and Ning Xu. Hierarchical image generation via transformer-based sequential patch selection. In Proceedings of the AAAI Conference on Arti- ficial Intelligence, pages 2938-2945, 2022.","title":"Hierarchical image generation via transformer-based sequential patch selection","authors":"Xu ; ] Xiaogang Xu, Ning Xu, Xu","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xu et al., 2017] Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative mes- sage passing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5410- 5419, 2017.","title":"Scene graph generation by iterative message passing","authors":"Danfei Xu, Yuke Zhu, Christopher B Choy, Li Fei-Fei","year":"2017","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xu et al., 2018] Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xi- aodong He. AttnGAN: Fine-grained text to image gener- ation with attentional generative adversarial networks. In 2018 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 1316-1324, 2018.","title":"AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks","authors":"Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He","year":"2018","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yang et al., 2022] Zuopeng Yang, Daqing Liu, Chaoyue Wang, Jie Yang, and Dacheng Tao. Modeling image com- position for complex scene generation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7754-7763, 2022.","title":"Modeling image composition for complex scene generation","authors":"Yang","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Ye et al., 2019] Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, and Mingzhong Wang. A vectorized relational graph con- volutional network for multi-relational network alignment. In International Joint Conferences on Artificial Intelli- gence, pages 4135-4141, 2019.","title":"A vectorized relational graph convolutional network for multi-relational network alignment","authors":"Rui Ye, Xin Li, Yujie Fang, Hongyu Zang, Mingzhong Wang","year":"2019","venue":"International Joint Conferences on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2019/574"},{"position":35,"raw":"[Yu et al., 2021] Jing Yu, Yuan Chai, Yujing Wang, Yue Hu, and Qi Wu. CogTree: Cognition tree loss for unbiased scene graph generation. In Zhi-Hua Zhou, editor, Pro- ceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 1274-1280. In- ternational Joint Conferences on Artificial Intelligence Or- ganization, 8 2021.","title":"CogTree: Cognition tree loss for unbiased scene graph generation","authors":"Jing Yu, Yuan Chai, Yujing Wang, Yue Hu, Qi Wu","year":"2021","venue":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21","doi":null,"url":null,"internal_url":"/proceedings/2021/176"},{"position":36,"raw":"[Zhang et al., 2018] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018.","title":"The unreasonable effectiveness of deep features as a perceptual metric","authors":"Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, Oliver Wang","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2021] Wensheng Zhang, Yan Zheng, Taiga Miyazono, Seiichi Uchida, and Brian Kenji Iwana. To- wards book cover design via layout graphs. In Josep Lladós, Daniel Lopresti, and Seiichi Uchida, editors, Inter- national Conference on Document Analysis and Recogni- tion, pages 642-657, Cham, 2021. Springer International Publishing.","title":"Towards book cover design via layout graphs","authors":"Wensheng Zhang, Yan Zheng, Taiga Miyazono, Seiichi Uchida, Brian Kenji Iwana","year":"2021","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2022] Tianyu Zhang, Xu Du, Chia-Ming Chang, Xi Yang, and Haoran Xie. Interactive drawing in- terface for editing scene graph. International Conference on Cyberworlds (CW), pages 171-172, 2022.","title":"Interactive drawing interface for editing scene graph","authors":"Tianyu Zhang, Xu Du, Chia-Ming Chang, Xi Yang, Haoran Xie","year":"2022","venue":"International Conference on Cyberworlds (CW)","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhao et al., 2019] Bo Zhao, Lili Meng, Weidong Yin, and Leonid Sigal. Image generation from layout. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8584-8593, 2019.","title":"Image generation from layout","authors":"Bo Zhao, Lili Meng, Weidong Yin, Leonid Sigal","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhao et al., 2022] Xin Zhao, Lei Wu, Xu Chen, and Bin Gong. High-quality image generation from scene graphs with transformer. In 2022 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2022.","title":"High-quality image generation from scene graphs with transformer","authors":"Xin Zhao, Lei Wu, Xu Chen, Bin Gong","year":"2022","venue":"IEEE International Conference on Multimedia and Expo (ICME)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}