{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T18:45:53Z","timestamp":1785523553356,"version":"3.56.0"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC"},{"name":"Supercomputing Center of the USTC"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            In complex scene synthesis, the effective representation of layouts is paramount. This paper introduces LayoutEnc, an advanced approach specifically designed to enhance layout representation by improving interpretability, robustness, and expressiveness, thereby facilitating more efficient image transformation. Distinct from conventional approaches that homogenize layout and image data, LayoutEnc distinctively processes various data modalities, enhancing the fidelity and interpretability of the layout representation. We apply stochastic noise injection to image tokens to align training and inference conditions, thereby fortifying the robustness of the layout representation. Additionally, LayoutEnc employs a two-stage multi-scale guidance learning strategy, to meticulously extract and refine semantic and textural features from training images. This enriched layout representation is then adeptly integrated into a transformer-based image generation framework, facilitating controlled and nuanced scene synthesis. Experimental results on the COCO-stuff and Visual Genome datasets demonstrate that LayoutEnc outperforms prior works in metrics such as FID and Scene-FID scores. The code and demo are available on\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/qsun1\/LayoutEnc\">https:\/\/github.com\/qsun1\/LayoutEnc<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3716389","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T15:50:51Z","timestamp":1738943451000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["LayoutEnc: Leveraging Enhanced Layout Representations for Transformer-based Complex Scene Synthesis"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4677-8834","authenticated-orcid":false,"given":"Xiao","family":"Cui","sequence":"first","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5204-765X","authenticated-orcid":false,"given":"Qi","family":"Sun","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3048-6980","authenticated-orcid":false,"given":"Min","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-6263","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1690-9836","authenticated-orcid":false,"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2188-3028","authenticated-orcid":false,"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[{"name":"MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3617597","article-title":"Composed image retrieval using contrastive learning and task-oriented clip-based features","volume":"20","author":"Baldrati Alberto","year":"2023","unstructured":"Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. 2023. Composed image retrieval using contrastive learning and task-oriented clip-based features. TOMM 20, 3 (2023), 1\u201324.","journal-title":"TOMM"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_1_4_2","unstructured":"Alexey Bochkovskiy Chien-Yao Wang and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934. Retrieved from https:\/\/arxiv.org\/pdf\/2004.10934"},{"key":"e_1_3_1_5_2","article-title":"Language models are few-shot learners","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In NeurIPS.","journal-title":"NeurIPS"},{"key":"e_1_3_1_6_2","first-page":"1209","article-title":"COCO-Stuff: Thing and stuff classes in context","author":"Caesar Holger","year":"2018","unstructured":"Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. 2018. COCO-Stuff: Thing and stuff classes in context. In CVPR. 1209\u20131218.","journal-title":"CVPR"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Nicolas Carion Francisco Massa Gabriel Synnaeve Nicolas Usunier Alexander Kirillov and Sergey Zagoruyko. 2020. End-to-end object detection with transformers. arXiv:2005.12872. Retrieved from https:\/\/arxiv.org\/pdf\/2005.12872","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_1_8_2","first-page":"6633","article-title":"Group DETR: Fast DETR training with group-wise one-to-many assignment","author":"Chen Qiang","year":"2023","unstructured":"Qiang Chen, Xiaokang Chen, Jian Wang, Shan Zhang, Kun Yao, Haocheng Feng, Junyu Han, Errui Ding, Gang Zeng, and Jingdong Wang. 2023. Group DETR: Fast DETR training with group-wise one-to-many assignment. In CVPR. 6633\u20136642.","journal-title":"CVPR"},{"key":"e_1_3_1_9_2","unstructured":"Ting Chen Saurabh Saxena Lala Li David J Fleet and Geoffrey Hinton. 2021. Pix2Seq: A language modeling framework for object detection. arXiv:2109.10852. Retrieved from https:\/\/arxiv.org\/pdf\/2109.10852"},{"key":"e_1_3_1_10_2","unstructured":"Ting Chen Saurabh Saxena Lala Li Tsung-Yi Lin David J. Fleet and Geoffrey Hinton. 2022. A unified sequence interface for vision tasks. arXiv:2206.07669. Retrieved from https:\/\/arxiv.org\/pdf\/2206.07669"},{"key":"e_1_3_1_11_2","first-page":"4327","article-title":"Background layout generation and object knowledge transfer for text-to-image generation","author":"Chen Zhuowei","year":"2022","unstructured":"Zhuowei Chen, Zhendong Mao, Shancheng Fang, and Bo Hu. 2022. Background layout generation and object knowledge transfer for text-to-image generation. In ACM MM, 4327\u20134335.","journal-title":"ACM MM"},{"key":"e_1_3_1_12_2","article-title":"SinKD: Sinkhorn distance minimization for knowledge distillation","author":"Cui Xiao","year":"2024","unstructured":"Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, and Houqiang Li. 2024a. SinKD: Sinkhorn distance minimization for knowledge distillation. In TNNLS (2024).","journal-title":"TNNLS"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Xiao Cui Yulei Qin Yuting Gao Enwei Zhang Zihan Xu Tong Wu Ke Li Xing Sun Wengang Zhou and Houqiang Li. 2024. Sinkhorn distance minimization for knowledge distillation. arXiv:2402.17110. Retrieved from https:\/\/arxiv.org\/pdf\/2402.17110","DOI":"10.1109\/TNNLS.2024.3501335"},{"key":"e_1_3_1_14_2","unstructured":"Xiao Cui Mo Zhu Yulei Qin Liang Xie Wengang Zhou and Houqiang Li. 2024. Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models. arXiv:2412.14528. Retrieved from https:\/\/arxiv.org\/pdf\/2412.14528"},{"key":"e_1_3_1_15_2","article-title":"CogView: Mastering text-to-image generation via transformers","author":"Ding Ming","year":"2021","unstructured":"Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. 2021. CogView: Mastering text-to-image generation via transformers. In NeurIPS.","journal-title":"NeurIPS"},{"key":"e_1_3_1_16_2","first-page":"12873","article-title":"Taming transformers for high-resolution image synthesis","author":"Esser Patrick","year":"2021","unstructured":"Patrick Esser, Robin Rombach, and Bjorn Ommer. 2021. Taming transformers for high-resolution image synthesis. In CVPR, 12873\u201312883.","journal-title":"CVPR"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1609\/aaai.v37i1.25133","article-title":"Frido: Feature pyramid diffusion for complex scene image synthesis","volume":"37","author":"Fan Wan-Cyuan","year":"2023","unstructured":"Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, and Yu-Chiang Frank Wang. 2023. Frido: Feature pyramid diffusion for complex scene image synthesis. In AAAI, Vol. 37, 579\u2013587.","journal-title":"AAAI"},{"key":"e_1_3_1_18_2","unstructured":"Xiuye Gu Tsung-Yi Lin Weicheng Kuo and Yin Cui. 2021. Open-vocabulary object detection via vision and language knowledge distillation. arXiv:2104.13921. Retrieved from https:\/\/arxiv.org\/pdf\/2104.13921"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3248143"},{"key":"e_1_3_1_20_2","first-page":"15049","article-title":"Context-aware layout to image generation with enhanced object appearance","author":"He Sen","year":"2021","unstructured":"Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, and Tao Xiang. 2021. Context-aware layout to image generation with enhanced object appearance. In CVPR. 15049\u201315058.","journal-title":"CVPR"},{"key":"e_1_3_1_21_2","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler G\u00fcnter Klambauer and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a Nash equilibrium. arXiv:1706.08500. Retrieved from https:\/\/arxiv.org\/pdf\/1706.08500"},{"key":"e_1_3_1_22_2","article-title":"Denoising diffusion probabilistic models","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In NeurIPS.","journal-title":"NeurIPS"},{"key":"e_1_3_1_23_2","first-page":"2796","article-title":"Few-shot image generation using discrete content representation","author":"Hong Yan","year":"2022","unstructured":"Yan Hong, Li Niu, Jianfu Zhang, and Liqing Zhang. 2022. Few-shot image generation using discrete content representation. In ACM MM, 2796\u20132804.","journal-title":"ACM MM"},{"issue":"7","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3653714","article-title":"Building category graphs representation with spatial and temporal attention for visual navigation","volume":"20","author":"Hu Xiaobo","year":"2024","unstructured":"Xiaobo Hu, Youfang Lin, Hehe Fan, Shuo Wang, Zhihao Wu, and Kai Lv. 2024. Building category graphs representation with spatial and temporal attention for visual navigation. TOMM 20, 7 (2024), 1\u201322.","journal-title":"TOMM"},{"key":"e_1_3_1_25_2","first-page":"1706","article-title":"A benchmark for controllable text-image-to-video generation","volume":"26","author":"Hu Yaosi","year":"2023","unstructured":"Yaosi Hu, Chong Luo, and Zhenzhong Chen. 2023. A benchmark for controllable text-image-to-video generation. TMM 26 (2023), 1706\u20131719.","journal-title":"TMM"},{"issue":"6","key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3648370","article-title":"Region-focused network for dense captioning","volume":"20","author":"Huang Qingbao","year":"2024","unstructured":"Qingbao Huang, Pijian Li, Youji Huang, Feng Shuang, and Yi Cai. 2024. Region-focused network for dense captioning. TOMM 20, 6 (2024), 1\u201320.","journal-title":"TOMM"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5143773"},{"key":"e_1_3_1_28_2","unstructured":"Manuel Jahn Robin Rombach and Bj\u00f6rn Ommer. 2021. High-resolution complex scene synthesis with transformers. arXiv:2105.06458. Retrieved from https:\/\/arxiv.org\/pdf\/2105.06458"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0981-7"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_1_31_2","first-page":"1829","article-title":"Lightweight text-driven image editing with disentangled content and attributes","volume":"26","author":"Li Bo","year":"2023","unstructured":"Bo Li, Xiao Lin, Bin Liu, Zhi-Fen He, and Yu-Kun Lai. 2023. Lightweight text-driven image editing with disentangled content and attributes. TMM 26 (2023), 1829\u20131841.","journal-title":"TMM"},{"key":"e_1_3_1_32_2","first-page":"18558","article-title":"Lite DETR: An interleaved multi-scale encoder for efficient DETR","author":"Li Feng","year":"2023","unstructured":"Feng Li, Ailing Zeng, Shilong Liu, Hao Zhang, Hongyang Li, Lei Zhang, and Lionel M Ni. 2023. Lite DETR: An interleaved multi-scale encoder for efficient DETR. In CVPR, 18558\u201318567.","journal-title":"CVPR"},{"key":"e_1_3_1_33_2","first-page":"13619","article-title":"DN-DETR: Accelerate DETR training by introducing query denoising","author":"Li Feng","year":"2022","unstructured":"Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. 2022. DN-DETR: Accelerate DETR training by introducing query denoising. In CVPR, 13619\u201313627.","journal-title":"CVPR"},{"key":"e_1_3_1_34_2","first-page":"12174","article-title":"Object-driven text-to-image synthesis via adversarial training","author":"Li Wenbo","year":"2019","unstructured":"Wenbo Li, Pengchuan Zhang, Lei Zhang, Qiuyuan Huang, Xiaodong He, Siwei Lyu, and Jianfeng Gao. 2019. Object-driven text-to-image synthesis via adversarial training. In CVPR, 12174\u201312182.","journal-title":"CVPR"},{"key":"e_1_3_1_35_2","first-page":"22511","article-title":"Gligen: Open-set grounded text-to-image generation","author":"Li Yuheng","year":"2023","unstructured":"Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. 2023b. Gligen: Open-set grounded text-to-image generation. In CVPR, 22511\u201322521.","journal-title":"CVPR"},{"key":"e_1_3_1_36_2","first-page":"13819","article-title":"Image synthesis from layout with locality-aware mask adaption","author":"Li Zejian","year":"2021","unstructured":"Zejian Li, Jingyu Wu, Immanuel Koh, Yongchuan Tang, and Lingyun Sun. 2021. Image synthesis from layout with locality-aware mask adaption. In ICCV, 13819\u201313828.","journal-title":"ICCV"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3274228"},{"key":"e_1_3_1_38_2","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"Lin Tsung-Yi","year":"2014","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In ECCV, 740\u2013755.","journal-title":"ECCV"},{"key":"e_1_3_1_39_2","first-page":"141","article-title":"Sounding video generator: A unified framework for text-guided sounding video generation","volume":"26","author":"Liu Jiawei","year":"2023","unstructured":"Jiawei Liu, Weining Wang, Sihan Chen, Xinxin Zhu, and Jing Liu. 2023. Sounding video generator: A unified framework for text-guided sounding video generation. TMM 26 (2023), 141\u2013153.","journal-title":"TMM"},{"key":"e_1_3_1_40_2","first-page":"3651","article-title":"Conditional DETR for fast training convergence","author":"Meng Depu","year":"2021","unstructured":"Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang. 2021. Conditional DETR for fast training convergence. In ICCV, 3651\u20133660.","journal-title":"ICCV"},{"key":"e_1_3_1_41_2","unstructured":"Ron Mokady Amir Hertz and Amit H Bermano. 2021. ClipCap: CLIP prefix for image captioning. arXiv:2111.09734. Retrieved from https:\/\/arxiv.org\/pdf\/2111.09734"},{"key":"e_1_3_1_42_2","unstructured":"OpenAI. 2023. GPT4 technical report. arXiv:2303.08774. Retrieved from https:\/\/arxiv.org\/pdf\/2303.08774"},{"key":"e_1_3_1_43_2","unstructured":"Justin NM Pinkney and Chuan Li. 2022. CLIP2latent: Text driven sampling of a pre-trained stylegan using denoising diffusion and CLIP. arXiv:2210.02347. Retrieved from https:\/\/arxiv.org\/pdf\/2210.02347"},{"key":"e_1_3_1_44_2","first-page":"14152","article-title":"Masked AutoDecoder is effective multi-task vision generalist","author":"Qiu Han","year":"2024","unstructured":"Han Qiu, Jiaxing Huang, Peng Gao, Lewei Lu, Xiaoqin Zhang, and Shijian Lu. 2024. Masked AutoDecoder is effective multi-task vision generalist. In CVPR, 14152\u201314161.","journal-title":"CVPR"},{"key":"e_1_3_1_45_2","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In ICML, 8748\u20138763.","journal-title":"ICML"},{"key":"e_1_3_1_46_2","article-title":"Language models are unsupervised multitask learners","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog (2019).","journal-title":"OpenAI blog"},{"key":"e_1_3_1_47_2","unstructured":"Aditya Ramesh Prafulla Dhariwal Alex Nichol Casey Chu and Mark Chen. 2022. Hierarchical text-conditional image generation with CLIP latents. arXiv:2204.06125. Retrieved from https:\/\/arxiv.org\/pdf\/2204.06125"},{"key":"e_1_3_1_48_2","first-page":"8821","article-title":"Zero-shot text-to-image generation","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In ICML, 8821\u20138831.","journal-title":"ICML"},{"key":"e_1_3_1_49_2","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach Robin","year":"2022","unstructured":"Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj\u00f6rn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In CVPR, 10684\u201310695.","journal-title":"CVPR"},{"key":"e_1_3_1_50_2","article-title":"Improved techniques for training GANs","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In NeurIPS.","journal-title":"NeurIPS"},{"key":"e_1_3_1_51_2","first-page":"10531","article-title":"Image synthesis from reconfigurable layout and style","author":"Sun Wei","year":"2019","unstructured":"Wei Sun and Tianfu Wu. 2019. Image synthesis from reconfigurable layout and style. In ICCV, 10531\u201310540.","journal-title":"ICCV"},{"issue":"9","key":"e_1_3_1_52_2","first-page":"5070","article-title":"Learning layout and style reconfigurable GANs for controllable image synthesis","volume":"44","author":"Sun Wei","year":"2021","unstructured":"Wei Sun and Tianfu Wu. 2021. Learning layout and style reconfigurable GANs for controllable image synthesis. TPAMI 44, 9 (2021), 5070\u20135087.","journal-title":"TPAMI"},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1609\/aaai.v35i3.16368","article-title":"Object-centric image generation from layouts","volume":"35","author":"Sylvain Tristan","year":"2021","unstructured":"Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R Devon Hjelm, and Shikhar Sharma. 2021. Object-centric image generation from layouts. In AAAI, Vol. 35, 2647\u20132655.","journal-title":"AAAI"},{"key":"e_1_3_1_54_2","first-page":"14214","article-title":"GALIP: Generative adversarial CLIPs for text-to-image synthesis","author":"Tao Ming","year":"2023","unstructured":"Ming Tao, Bing-Kun Bao, Hao Tang, and Changsheng Xu. 2023. GALIP: Generative adversarial CLIPs for text-to-image synthesis. In CVPR, 14214\u201314223.","journal-title":"CVPR"},{"issue":"11","key":"e_1_3_1_55_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten Laurens","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR 9, 11 (2008), 2579\u20132605.","journal-title":"JMLR"},{"key":"e_1_3_1_56_2","article-title":"Attention is all you need","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS.","journal-title":"NeurIPS"},{"key":"e_1_3_1_57_2","first-page":"7246","article-title":"Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception","author":"Wang Yibo","year":"2024","unstructured":"Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, et al. 2024. Detdiffusion: Synergizing generative and perceptive models for enhanced data generation and perception. In CVPR, 7246\u20137255.","journal-title":"CVPR"},{"key":"e_1_3_1_58_2","first-page":"18134","article-title":"GroupViT: Semantic segmentation emerges from text supervision","author":"Xu Jiarui","year":"2022","unstructured":"Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. 2022. GroupViT: Semantic segmentation emerges from text supervision. In CVPR, 18134\u201318144.","journal-title":"CVPR"},{"key":"e_1_3_1_59_2","first-page":"20908","article-title":"Dream3D: Zero-shot text-to-3D synthesis using 3D shape prior and text-to-image diffusion models","author":"Xu Jiale","year":"2023","unstructured":"Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. 2023. Dream3D: Zero-shot text-to-3D synthesis using 3D shape prior and text-to-image diffusion models. In CVPR, 20908\u201320918.","journal-title":"CVPR"},{"key":"e_1_3_1_60_2","unstructured":"Ling Yang Zhilong Zhang Zhaochen Yu Jingwei Liu Minkai Xu Stefano Ermon and Bin Cui. 2024. Cross-modal contextualized diffusion models for text-guided visual generation and editing. arXiv:2402.16627. Retrieved from https:\/\/arxiv.org\/pdf\/2402.16627"},{"key":"e_1_3_1_61_2","first-page":"7764","article-title":"Modeling image composition for complex scene generation","author":"Yang Zuopeng","year":"2022","unstructured":"Zuopeng Yang, Daqing Liu, Chaoyue Wang, Jie Yang, and Dacheng Tao. 2022. Modeling image composition for complex scene generation. In CVPR, 7764\u20137773.","journal-title":"CVPR"},{"issue":"5","key":"e_1_3_1_62_2","first-page":"1","article-title":"Multi-object tracking with spatial-temporal tracklet association","volume":"20","author":"You Sisi","year":"2024","unstructured":"Sisi You, Hantao Yao, Bing-Kun Bao, and Changsheng Xu. 2024. Multi-object tracking with spatial-temporal tracklet association. TOMM 20, 5 (2024), 1\u201321.","journal-title":"TOMM"},{"key":"e_1_3_1_63_2","first-page":"22468","article-title":"SceneComposer: Any-level semantic image synthesis","author":"Zeng Yu","year":"2023","unstructured":"Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, John Collomosse, Jason Kuen, and Vishal M. Patel. 2023. SceneComposer: Any-level semantic image synthesis. In CVPR, 22468\u201322478.","journal-title":"CVPR"},{"key":"e_1_3_1_64_2","first-page":"586","article-title":"The unreasonable effectiveness of deep features as a perceptual metric","author":"Zhang Richard","year":"2018","unstructured":"Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 586\u2013595.","journal-title":"CVPR"},{"key":"e_1_3_1_65_2","first-page":"8584","article-title":"Image generation from layout","author":"Zhao Bo","year":"2019","unstructured":"Bo Zhao, Lili Meng, Weidong Yin, and Leonid Sigal. 2019. Image generation from layout. In CVPR, 8584\u20138593.","journal-title":"CVPR"},{"issue":"10","key":"e_1_3_1_66_2","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1007\/s11263-020-01300-7","article-title":"Layout2image: Image generation from layout","volume":"128","author":"Zhao Bo","year":"2020","unstructured":"Bo Zhao, Weidong Yin, Lili Meng, and Leonid Sigal. 2020. Layout2image: Image generation from layout. IJCV 128, 10 (2020), 2418\u20132435.","journal-title":"IJCV"},{"key":"e_1_3_1_67_2","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","author":"Zhu Xizhou","year":"2021","unstructured":"Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. 2021. Deformable DETR: Deformable transformers for end-to-end object detection. In ICLR.","journal-title":"ICLR"},{"issue":"9","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3673231","article-title":"Unified view empirical study for large pretrained model on cross-domain few-shot learning","volume":"20","author":"Zhuo Linhai","year":"2024","unstructured":"Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, and Yu-Gang Jiang. 2024. Unified view empirical study for large pretrained model on cross-domain few-shot learning. TOMM 20, 9 (2024), 1\u201318.","journal-title":"TOMM"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716389","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3716389","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:43Z","timestamp":1750272223000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3716389"],"URL":"https:\/\/doi.org\/10.1145\/3716389","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]},"assertion":[{"value":"2024-07-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}