Academic literature on the topic 'Scene Graph Generation'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Scene Graph Generation.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Scene Graph Generation"
Khademi, Mahmoud, and Oliver Schulte. "Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11237–45. http://dx.doi.org/10.1609/aaai.v34i07.6783.
Full textHua, Tianyu, Hongdong Zheng, Yalong Bai, Wei Zhang, Xiao-Ping Zhang, and Tao Mei. "Exploiting Relationship for Complex-scene Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1584–92. http://dx.doi.org/10.1609/aaai.v35i2.16250.
Full textWald, Johanna, Nassir Navab, and Federico Tombari. "Learning 3D Semantic Scene Graphs with Instance Embeddings." International Journal of Computer Vision 130, no. 3 (January 22, 2022): 630–51. http://dx.doi.org/10.1007/s11263-021-01546-9.
Full textBauer, Daniel. "Understanding Descriptions of Visual Scenes Using Graph Grammars." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1656–57. http://dx.doi.org/10.1609/aaai.v27i1.8498.
Full textShao, Tong, and Dapeng Oliver Wu. "Graph-LSTM with Global Attribute for Scene Graph Generation." Journal of Physics: Conference Series 2003, no. 1 (August 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2003/1/012001.
Full textLin, Bingqian, Yi Zhu, and Xiaodan Liang. "Atom correlation based graph propagation for scene graph generation." Pattern Recognition 122 (February 2022): 108300. http://dx.doi.org/10.1016/j.patcog.2021.108300.
Full textWang, Ruize, Zhongyu Wei, Piji Li, Qi Zhang, and Xuanjing Huang. "Storytelling from an Image Stream Using Scene Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9185–92. http://dx.doi.org/10.1609/aaai.v34i05.6455.
Full textChen, Jin, Xiaofeng Ji, and Xinxiao Wu. "Adaptive Image-to-Video Scene Graph Generation via Knowledge Reasoning and Adversarial Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 276–84. http://dx.doi.org/10.1609/aaai.v36i1.19903.
Full textJung, Gayoung, Jonghun Lee, and Incheol Kim. "Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation." Sensors 21, no. 9 (May 2, 2021): 3164. http://dx.doi.org/10.3390/s21093164.
Full textLi, Shuohao, Min Tang, Jun Zhang, and Lincheng Jiang. "Attentive Gated Graph Neural Network for Image Scene Graph Generation." Symmetry 12, no. 4 (April 2, 2020): 511. http://dx.doi.org/10.3390/sym12040511.
Full textDissertations / Theses on the topic "Scene Graph Generation"
Nguyen, Duc Minh Chau. "Affordance learning for visual-semantic perception." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2443.
Full textGarrett, Austin J. "Infrastructure for modeling and inference engineering with 3D generative scene graphs." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130688.
Full textCataloged from the official PDF of thesis.
Includes bibliographical references (pages 67-68).
Recent advances in probabilistic programming have enabled the development of probabilistic generative models for visual perception using a rich abstract representation of 3D scene geometry called a scene graph. However, there remain several challenges in the practical implementation of scene graph models, including human-editable specification, visualization, priors, structure inference, hyperparameters tuning, and benchmarking. In this thesis, I describe the development of infrastructure to enable the development and research of scene graph models by researchers and practitioners. A description of a preliminary scene graph model and inference program for 3D scene structure is provided, along with an implementation in the probabilistic programming language Gen. Utilities for visualizing and understanding distributions over scene graphs are developed. Synthetic enumerative tests of the posterior and inference algorithm are conducted, and conclusions drawn for the improvement of the proposed modeling components. Finally, I collect and analyze real-world scene graph data, and use it to optimize model hyperparameters; the preliminary structure inference program is then tested in a structure prediction task with both the unoptimized and optimized models.
by Austin J. Garrett.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Wang, Tse-Hsien, and 汪澤先. "Interactive Background Scene Generation: Controllable Animation based on Motion Graph." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/20054229214556970499.
Full text國立臺灣大學
資訊網路與多媒體研究所
97
In this paper, an interactive background scene generation and editing system is proposed based on improved motion graph. By analyzing the motion of an input animation with limited length, our system could synthesize large amount of various motions to yield a composting scene animation with unlimited length by connecting the input motion pieces through smooth transitions based on a motion graph layer, which is generated by using randomized cuts and further analysis on time domain. The smooth transitions are obtained by searching the best path according to specified circumstances. Finally the result is optimized by repeatedly substituting animation subsequences. The user can interactively specify some physical constraints of the scene on keyframes, such as wind direction or velocity of flow, even one simple path for character to follow, and the system would automatically generate continuous and natural motion in accordance with them.
Books on the topic "Scene Graph Generation"
Coolen, A. C. C., A. Annibale, and E. S. Roberts. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0001.
Full textBook chapters on the topic "Scene Graph Generation"
Yang, Jingkang, Yi Zhe Ang, Zujin Guo, Kaiyang Zhou, Wayne Zhang, and Ziwei Liu. "Panoptic Scene Graph Generation." In Lecture Notes in Computer Science, 178–96. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19812-0_11.
Full textYang, Jianwei, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. "Graph R-CNN for Scene Graph Generation." In Computer Vision – ECCV 2018, 690–706. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01246-5_41.
Full textKumar, Vishal, Albert Mundu, and Satish Kumar Singh. "Scene Graph Generation with Geometric Context." In Communications in Computer and Information Science, 340–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11346-8_30.
Full textSu, Xia, Chenglin Wu, Wen Gao, and Weixin Huang. "Interior Layout Generation Based on Scene Graph and Graph Generation Model." In Design Computing and Cognition’20, 267–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90625-2_15.
Full textKhademi, Mahmoud, and Oliver Schulte. "Dynamic Gated Graph Neural Networks for Scene Graph Generation." In Computer Vision – ACCV 2018, 669–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20876-9_42.
Full textZareian, Alireza, Zhecan Wang, Haoxuan You, and Shih-Fu Chang. "Learning Visual Commonsense for Robust Scene Graph Generation." In Computer Vision – ECCV 2020, 642–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58592-1_38.
Full textZhou, Fangbo, Huaping Liu, Xinghang Li, and Huailin Zhao. "MCTS-Based Robotic Exploration for Scene Graph Generation." In Communications in Computer and Information Science, 403–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9247-5_31.
Full textZhang, Ao, Yuan Yao, Qianyu Chen, Wei Ji, Zhiyuan Liu, Maosong Sun, and Tat-Seng Chua. "Fine-Grained Scene Graph Generation with Data Transfer." In Lecture Notes in Computer Science, 409–24. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19812-0_24.
Full textWang, Wenbin, Ruiping Wang, Shiguang Shan, and Xilin Chen. "Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation." In Computer Vision – ECCV 2020, 222–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58601-0_14.
Full textHerzig, Roei, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, and Amir Globerson. "Learning Canonical Representations for Scene Graph to Image Generation." In Computer Vision – ECCV 2020, 210–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58574-7_13.
Full textConference papers on the topic "Scene Graph Generation"
Garg, Sarthak, Helisa Dhamo, Azade Farshad, Sabrina Musatian, Nassir Navab, and Federico Tombari. "Unconditional Scene Graph Generation." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01605.
Full textGuo, Yuyu, Jingkuan Song, Lianli Gao, and Heng Tao Shen. "One-shot Scene Graph Generation." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3414025.
Full textLiu, Hengyue, Ning Yan, Masood Mortazavi, and Bir Bhanu. "Fully Convolutional Scene Graph Generation." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01138.
Full textKhandelwal, Siddhesh, Mohammed Suhail, and Leonid Sigal. "Segmentation-grounded Scene Graph Generation." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01558.
Full textYu, Jing, Yuan Chai, Yujing Wang, Yue Hu, and Qi Wu. "CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/176.
Full textZhang, Zhichao, Junyu Dong, Qilu Zhao, Lin Qi, and Shu Zhang. "Attention LSTM for Scene Graph Generation." In 2021 6th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2021. http://dx.doi.org/10.1109/icivc52351.2021.9526967.
Full textHe, Yunqing, Tongwei Ren, Jinhui Tang, and Gangshan Wu. "Heterogeneous Learning for Scene Graph Generation." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548356.
Full textChen, Min, Xinyu Lyu, Yuyu Guo, Jingwei Liu, Lianli Gao, and Jingkuan Song. "Multi-Scale Graph Attention Network for Scene Graph Generation." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859970.
Full textYu, Xiang, Ruoxin Chen, Jie Li, Jiawei Sun, Shijing Yuan, Huxiao Ji, Xinyu Lu, and Chentao Wu. "Zero-Shot Scene Graph Generation with Knowledge Graph Completion." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859944.
Full textTang, Kaihua, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. "Unbiased Scene Graph Generation From Biased Training." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00377.
Full text