Auswahl der wissenschaftlichen Literatur zum Thema „Hierarchical Pooling“
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Zeitschriftenartikel zum Thema "Hierarchical Pooling"
Fernando, Basura, und Stephen Gould. „Discriminatively Learned Hierarchical Rank Pooling Networks“. International Journal of Computer Vision 124, Nr. 3 (24.06.2017): 335–55. http://dx.doi.org/10.1007/s11263-017-1030-x.
Der volle Inhalt der QuelleRanjan, Ekagra, Soumya Sanyal und Partha Talukdar. „ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 5470–77. http://dx.doi.org/10.1609/aaai.v34i04.5997.
Der volle Inhalt der QuelleChen, Jiawang, und Zhenqiang Wu. „Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling“. Applied Sciences 12, Nr. 19 (28.09.2022): 9795. http://dx.doi.org/10.3390/app12199795.
Der volle Inhalt der QuelleGrumitt, R. D. P., Luke R. P. Jew und C. Dickinson. „Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler“. Monthly Notices of the Royal Astronomical Society 496, Nr. 4 (26.06.2020): 4383–401. http://dx.doi.org/10.1093/mnras/staa1857.
Der volle Inhalt der QuelleDevineni, Naresh, Upmanu Lall, Neil Pederson und Edward Cook. „A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression“. Journal of Climate 26, Nr. 12 (15.06.2013): 4357–74. http://dx.doi.org/10.1175/jcli-d-11-00675.1.
Der volle Inhalt der QuelleChen, Junying, und Ying Chen. „Saliency Enhanced Hierarchical Bilinear Pooling for Fine-Grained Classification“. Journal of Computer-Aided Design & Computer Graphics 33, Nr. 2 (01.02.2021): 241–49. http://dx.doi.org/10.3724/sp.j.1089.2021.18399.
Der volle Inhalt der QuelleSanchez-Giraldo, Luis G., Md Nasir Uddin Laskar und Odelia Schwartz. „Normalization and pooling in hierarchical models of natural images“. Current Opinion in Neurobiology 55 (April 2019): 65–72. http://dx.doi.org/10.1016/j.conb.2019.01.008.
Der volle Inhalt der QuelleTan, Min, Fu Yuan, Jun Yu, Guijun Wang und Xiaoling Gu. „Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling“. ACM Transactions on Multimedia Computing, Communications, and Applications 18, Nr. 1s (28.02.2022): 1–23. http://dx.doi.org/10.1145/3492221.
Der volle Inhalt der QuelleKo, Sung Moon, Sungjun Cho, Dae-Woong Jeong, Sehui Han, Moontae Lee und Honglak Lee. „Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 7 (26.06.2023): 8334–42. http://dx.doi.org/10.1609/aaai.v37i7.26005.
Der volle Inhalt der QuelleLi, Keqin. „Hierarchical Pooling Strategy Optimization for Accelerating Asymptomatic COVID-19 Screening“. IEEE Open Journal of the Computer Society 1 (2020): 276–84. http://dx.doi.org/10.1109/ojcs.2020.3036581.
Der volle Inhalt der QuelleDissertationen zum Thema "Hierarchical Pooling"
Mazari, Ahmed. „Apprentissage profond pour la reconnaissance d’actions en vidéos“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.
Der volle Inhalt der QuelleNowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained
Buchteile zum Thema "Hierarchical Pooling"
Zhang, Can, Yuexian Zou und Guang Chen. „Hierarchical Temporal Pooling for Efficient Online Action Recognition“. In MultiMedia Modeling, 471–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05710-7_39.
Der volle Inhalt der QuelleYu, Chaojian, Xinyi Zhao, Qi Zheng, Peng Zhang und Xinge You. „Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition“. In Computer Vision – ECCV 2018, 595–610. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01270-0_35.
Der volle Inhalt der QuelleLiu, Yan, Zhi Liu und Zhirong Lei. „Hierarchical Pooling Based Extreme Learning Machine for Image Classification“. In Lecture Notes in Electrical Engineering, 1–9. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9698-5_1.
Der volle Inhalt der QuelleThornton, John, Jolon Faichney, Michael Blumenstein und Trevor Hine. „Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling“. In AI 2008: Advances in Artificial Intelligence, 562–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_57.
Der volle Inhalt der QuelleFei, Xiaohan, Konstantine Tsotsos und Stefano Soatto. „A Simple Hierarchical Pooling Data Structure for Loop Closure“. In Computer Vision – ECCV 2016, 321–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46487-9_20.
Der volle Inhalt der QuelleLiu, Peishuo, Cangqi Zhou, Xiao Liu, Jing Zhang und Qianmu Li. „Multi-Granularity Contrastive Learning for Graph with Hierarchical Pooling“. In Artificial Neural Networks and Machine Learning – ICANN 2023, 499–511. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44216-2_41.
Der volle Inhalt der QuelleZhao, Haifeng, Xiaoping Wu, Dejun Bao und Shaojie Zhang. „Intracranial Hematoma Classification Based on the Pyramid Hierarchical Bilinear Pooling“. In Pattern Recognition and Computer Vision, 606–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88010-1_51.
Der volle Inhalt der QuelleLiu, Wenya, Zhi Yang, Haitao Gan, Zhongwei Huang, Ran Zhou und Ming Shi. „Hierarchical Pooling Graph Convolutional Neural Network for Alzheimer’s Disease Diagnosis“. In PRICAI 2023: Trends in Artificial Intelligence, 426–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7019-3_39.
Der volle Inhalt der QuelleBandyopadhyay, Sambaran, Manasvi Aggarwal und M. Narasimha Murty. „A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention“. In Advances in Knowledge Discovery and Data Mining, 554–65. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75762-5_44.
Der volle Inhalt der QuelleOtter, Thomas, und Tetyana Kosyakova. „Implications of Linear Versus Dummy Coding for Pooling of Information in Hierarchical Models“. In Quantitative Marketing and Marketing Management, 171–90. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-3722-3_8.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Hierarchical Pooling"
Pan, Zizheng, Bohan Zhuang, Jing Liu, Haoyu He und Jianfei Cai. „Scalable Vision Transformers with Hierarchical Pooling“. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00043.
Der volle Inhalt der QuelleFernando, Basura, Peter Anderson, Marcus Hutter und Stephen Gould. „Discriminative Hierarchical Rank Pooling for Activity Recognition“. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.212.
Der volle Inhalt der QuelleBi, Liande, Xin Sun, Fei Zhou und Junyu Dong. „Hierarchical Triplet Attention Pooling for Graph Classification“. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00100.
Der volle Inhalt der QuelleAli, Waqar, Sebastiano Vascon, Thilo Stadelmann und Marcello Pelillo. „Quasi-CliquePool: Hierarchical Graph Pooling for Graph Classification“. In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3555776.3578600.
Der volle Inhalt der QuelleRoy, Kashob Kumar, Amit Roy, A. K. M. Mahbubur Rahman, M. Ashraful Amin und Amin Ahsan Ali. „Structure-Aware Hierarchical Graph Pooling using Information Bottleneck“. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533778.
Der volle Inhalt der QuelleSu, Zidong, Zehui Hu und Yangding Li. „Hierarchical Graph Representation Learning with Local Capsule Pooling“. In MMAsia '21: ACM Multimedia Asia. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469877.3495645.
Der volle Inhalt der QuelleHe, Ke-Xin, Yu-Han Shen und Wei-Qiang Zhang. „Hierarchical Pooling Structure for Weakly Labeled Sound Event Detection“. In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2049.
Der volle Inhalt der QuelleRachmadi, Reza Fuad, Keiichi Uchimura, Gou Koutaki und Kohichi Ogata. „Hierarchical Spatial Pyramid Pooling for Fine-Grained Vehicle Classification“. In 2018 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2018. http://dx.doi.org/10.1109/iwbis.2018.8471695.
Der volle Inhalt der QuelleGao, Lijian, Ling Zhou, Qirong Mao und Ming Dong. „Adaptive Hierarchical Pooling for Weakly-supervised Sound Event Detection“. In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548097.
Der volle Inhalt der QuelleYu, Hualei, Yirong Yao, Jinliang Yuan und Chongjun Wang. „DIPool: Degree-Induced Pooling for Hierarchical Graph Representation Learning“. In 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2022. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00035.
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