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Статті в журналах з теми "Weakly-supervised semantic segmentation"
Zhang, Yachao, Zonghao Li, Yuan Xie, Yanyun Qu, Cuihua Li, and Tao Mei. "Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3421–29. http://dx.doi.org/10.1609/aaai.v35i4.16455.
Повний текст джерелаChen, Jie, Fen He, Yi Zhang, Geng Sun, and Min Deng. "SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion." Remote Sensing 12, no. 6 (March 24, 2020): 1049. http://dx.doi.org/10.3390/rs12061049.
Повний текст джерелаLi, Xueyi, Tianfei Zhou, Jianwu Li, Yi Zhou, and Zhaoxiang Zhang. "Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 1984–92. http://dx.doi.org/10.1609/aaai.v35i3.16294.
Повний текст джерелаCheng, Hao, Chaochen Gu, and Kaijie Wu. "Weakly-Supervised Semantic Segmentation via Self-training." Journal of Physics: Conference Series 1487 (March 2020): 012001. http://dx.doi.org/10.1088/1742-6596/1487/1/012001.
Повний текст джерелаOuassit, Youssef, Soufiane Ardchir, Mohammed Yassine El Ghoumari, and Mohamed Azouazi. "A Brief Survey on Weakly Supervised Semantic Segmentation." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 10 (July 26, 2022): 83–113. http://dx.doi.org/10.3991/ijoe.v18i10.31531.
Повний текст джерелаKim, Beomyoung, Sangeun Han, and Junmo Kim. "Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1754–61. http://dx.doi.org/10.1609/aaai.v35i2.16269.
Повний текст джерелаZhou, Tianfei, Liulei Li, Xueyi Li, Chun-Mei Feng, Jianwu Li, and Ling Shao. "Group-Wise Learning for Weakly Supervised Semantic Segmentation." IEEE Transactions on Image Processing 31 (2022): 799–811. http://dx.doi.org/10.1109/tip.2021.3132834.
Повний текст джерелаLi, Yi, Yanqing Guo, Yueying Kao, and Ran He. "Image Piece Learning for Weakly Supervised Semantic Segmentation." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 4 (April 2017): 648–59. http://dx.doi.org/10.1109/tsmc.2016.2623683.
Повний текст джерелаXiong, Changzhen, and Hui Zhi. "Multi-model Integrated Weakly Supervised Semantic Segmentation Method." Journal of Computer-Aided Design & Computer Graphics 31, no. 5 (2019): 800. http://dx.doi.org/10.3724/sp.j.1089.2019.17379.
Повний текст джерелаWang, Shuo, and Yizhou Wang. "Weakly Supervised Semantic Segmentation with a Multiscale Model." IEEE Signal Processing Letters 22, no. 3 (March 2015): 308–12. http://dx.doi.org/10.1109/lsp.2014.2358562.
Повний текст джерелаДисертації з теми "Weakly-supervised semantic segmentation"
Sawatzky, Johann [Verfasser]. "Weakly and Semi Supervised Semantic Segmentation of RGB Images / Johann Sawatzky." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1227990367/34.
Повний текст джерелаGötz, Michael [Verfasser], and R. [Akademischer Betreuer] Dillmann. "Variability-Aware and Weakly Supervised Learning for Semantic Tissue Segmentation / Michael Götz ; Betreuer: R. Dillmann." Karlsruhe : KIT-Bibliothek, 2017. http://d-nb.info/1137265000/34.
Повний текст джерелаGiraldo, Zuluaga Jhony Heriberto. "Graph-based Algorithms in Computer Vision, Machine Learning, and Signal Processing." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS037.
Повний текст джерелаGraph representation learning and its applications have gained significant attention in recent years. Notably, Graph Neural Networks (GNNs) and Graph Signal Processing (GSP) have been extensively studied. GNNs extend the concepts of convolutional neural networks to non-Euclidean data modeled as graphs. Similarly, GSP extends the concepts of classical digital signal processing to signals supported on graphs. GNNs and GSP have numerous applications such as semi-supervised learning, point cloud semantic segmentation, prediction of individual relations in social networks, modeling proteins for drug discovery, image, and video processing. In this thesis, we propose novel approaches in video and image processing, GNNs, and recovery of time-varying graph signals. Our main motivation is to use the geometrical information that we can capture from the data to avoid data hungry methods, i.e., learning with minimal supervision. All our contributions rely heavily on the developments of GSP and spectral graph theory. In particular, the sampling and reconstruction theory of graph signals play a central role in this thesis. The main contributions of this thesis are summarized as follows: 1) we propose new algorithms for moving object segmentation using concepts of GSP and GNNs, 2) we propose a new algorithm for weakly-supervised semantic segmentation using hypergraph neural networks, 3) we propose and analyze GNNs using concepts from GSP and spectral graph theory, and 4) we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples
Shen, Tong. "Context Learning and Weakly Supervised Learning for Semantic Segmentation." Thesis, 2018. http://hdl.handle.net/2440/120354.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
Ke, Zi-Yi, and 柯子逸. "Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/x3w74r.
Повний текст джерела國立清華大學
資訊系統與應用研究所
106
Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely on external models to infer pseudo pixel-level labels for training semantic segmentation models. In this thesis, we aim to develop a single neural network without resorting to any external models. We propose a novel self-guided strategy to fully utilize features learned across multiple levels to progressively generate the dense pseudo labels. First, we use high-level features as class-specific localization maps to roughly locate the classes. Next, we propose an affinity-guided method to encourage each localization map to be consistent with their intermediate level features. Third, we adopt the training image itself as guidance and propose a self-guided refinement to further transfer the image's inherent structure into the maps. Finally, we derive pseudo pixel-level labels from these localization maps and use the pseudo labels as ground truth to train the semantic segmentation model. Our proposed self-guided strategy is a unified framework, which is built on a single network and alternatively updates the feature representation and refines localization maps during the training procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that our method outperforms other weakly-supervised methods under the same setting.
Частини книг з теми "Weakly-supervised semantic segmentation"
Sun, Weixuan, Jing Zhang, and Nick Barnes. "3D Guided Weakly Supervised Semantic Segmentation." In Computer Vision – ACCV 2020, 585–602. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69525-5_35.
Повний текст джерелаChen, Liyi, Weiwei Wu, Chenchen Fu, Xiao Han, and Yuntao Zhang. "Weakly Supervised Semantic Segmentation with Boundary Exploration." In Computer Vision – ECCV 2020, 347–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58574-7_21.
Повний текст джерелаTokmakov, Pavel, Karteek Alahari, and Cordelia Schmid. "Weakly-Supervised Semantic Segmentation Using Motion Cues." In Computer Vision – ECCV 2016, 388–404. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46493-0_24.
Повний текст джерелаSun, Guoying, Meng Yang, and Wenfeng Luo. "Adversarial Decoupling for Weakly Supervised Semantic Segmentation." In Pattern Recognition and Computer Vision, 188–200. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88013-2_16.
Повний текст джерелаSun, Guolei, Wenguan Wang, Jifeng Dai, and Luc Van Gool. "Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation." In Computer Vision – ECCV 2020, 347–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_21.
Повний текст джерелаLiang, Binxiu, Yan Liu, Linxi He, and Jiangyun Li. "Weakly Supervised Semantic Segmentation Based on Deep Learning." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019), 455–64. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_43.
Повний текст джерелаFan, Junsong, Zhaoxiang Zhang, and Tieniu Tan. "Employing Multi-estimations for Weakly-Supervised Semantic Segmentation." In Computer Vision – ECCV 2020, 332–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58520-4_20.
Повний текст джерелаTan, Li, WenFeng Luo, and Meng Yang. "Weakly-Supervised Semantic Segmentation with Mean Teacher Learning." In Intelligence Science and Big Data Engineering. Visual Data Engineering, 324–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36189-1_27.
Повний текст джерелаAslan, Sinem, and Marcello Pelillo. "Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets." In Lecture Notes in Computer Science, 425–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30645-8_39.
Повний текст джерелаSang, Yu, Shi Li, and Yanfei Peng. "Multi-view Robustness-Enhanced Weakly Supervised Semantic Segmentation." In Intelligent Computing Theories and Application, 180–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13870-6_15.
Повний текст джерелаТези доповідей конференцій з теми "Weakly-supervised semantic segmentation"
Shen, Tong, Guosheng Lin, Lingqiao Liu, Chunhua Shen, and Ian Reid. "Weakly Supervised Semantic Segmentation Based on Co-segmentation." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.17.
Повний текст джерелаYe, Chaojie, Min Jiang, and Zhiming Luo. "Smoke Segmentation based on Weakly Supervised Semantic Segmentation." In 2022 12th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2022. http://dx.doi.org/10.1109/itme56794.2022.00082.
Повний текст джерелаZhang, Wei, Sheng Zeng, Dequan Wang, and Xiangyang Xue. "Weakly supervised semantic segmentation for social images." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298888.
Повний текст джерелаDong, Jiahua, Yang Cong, Gan Sun, and Dongdong Hou. "Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.01081.
Повний текст джерелаLu, Zheng, Dali Chen, and Dingyu Xue. "Survey of weakly supervised semantic segmentation methods." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8407307.
Повний текст джерелаFeng, Yanqing, and Lunwen Wang. "A Weakly-Supervised Approach for Semantic Segmentation." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729018.
Повний текст джерелаNivaggioli, Adrien, and Hicham Randrianarivo. "Weakly Supervised Semantic Segmentation of Satellite Images." In 2019 Joint Urban Remote Sensing Event (JURSE). IEEE, 2019. http://dx.doi.org/10.1109/jurse.2019.8809060.
Повний текст джерелаXing, Frank Z., Erik Cambria, Win-Bin Huang, and Yang Xu. "Weakly supervised semantic segmentation with superpixel embedding." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532562.
Повний текст джерелаZhang, Fei, Chaochen Gu, Chenyue Zhang, and Yuchao Dai. "Complementary Patch for Weakly Supervised Semantic Segmentation." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00715.
Повний текст джерелаZhu, Kaiyin, Neal N. Xiong, and Mingming Lu. "A Survey of Weakly-supervised Semantic Segmentation." In 2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 2023. http://dx.doi.org/10.1109/bigdatasecurity-hpsc-ids58521.2023.00013.
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