Literatura científica selecionada sobre o tema "Hierarchical Pooling"
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Artigos de revistas sobre o assunto "Hierarchical Pooling"
Fernando, Basura, e Stephen Gould. "Discriminatively Learned Hierarchical Rank Pooling Networks". International Journal of Computer Vision 124, n.º 3 (24 de junho de 2017): 335–55. http://dx.doi.org/10.1007/s11263-017-1030-x.
Texto completo da fonteRanjan, Ekagra, Soumya Sanyal e Partha Talukdar. "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5470–77. http://dx.doi.org/10.1609/aaai.v34i04.5997.
Texto completo da fonteChen, Jiawang, e Zhenqiang Wu. "Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling". Applied Sciences 12, n.º 19 (28 de setembro de 2022): 9795. http://dx.doi.org/10.3390/app12199795.
Texto completo da fonteGrumitt, R. D. P., Luke R. P. Jew e C. Dickinson. "Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler". Monthly Notices of the Royal Astronomical Society 496, n.º 4 (26 de junho de 2020): 4383–401. http://dx.doi.org/10.1093/mnras/staa1857.
Texto completo da fonteDevineni, Naresh, Upmanu Lall, Neil Pederson e Edward Cook. "A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression". Journal of Climate 26, n.º 12 (15 de junho de 2013): 4357–74. http://dx.doi.org/10.1175/jcli-d-11-00675.1.
Texto completo da fonteChen, Junying, e Ying Chen. "Saliency Enhanced Hierarchical Bilinear Pooling for Fine-Grained Classification". Journal of Computer-Aided Design & Computer Graphics 33, n.º 2 (1 de fevereiro de 2021): 241–49. http://dx.doi.org/10.3724/sp.j.1089.2021.18399.
Texto completo da fonteSanchez-Giraldo, Luis G., Md Nasir Uddin Laskar e Odelia Schwartz. "Normalization and pooling in hierarchical models of natural images". Current Opinion in Neurobiology 55 (abril de 2019): 65–72. http://dx.doi.org/10.1016/j.conb.2019.01.008.
Texto completo da fonteTan, Min, Fu Yuan, Jun Yu, Guijun Wang e Xiaoling Gu. "Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 1s (28 de fevereiro de 2022): 1–23. http://dx.doi.org/10.1145/3492221.
Texto completo da fonteKo, Sung Moon, Sungjun Cho, Dae-Woong Jeong, Sehui Han, Moontae Lee e Honglak Lee. "Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8334–42. http://dx.doi.org/10.1609/aaai.v37i7.26005.
Texto completo da fonteLi, 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.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteNowadays, 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
Capítulos de livros sobre o assunto "Hierarchical Pooling"
Zhang, Can, Yuexian Zou e 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.
Texto completo da fonteYu, Chaojian, Xinyi Zhao, Qi Zheng, Peng Zhang e 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.
Texto completo da fonteLiu, Yan, Zhi Liu e 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.
Texto completo da fonteThornton, John, Jolon Faichney, Michael Blumenstein e 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.
Texto completo da fonteFei, Xiaohan, Konstantine Tsotsos e 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.
Texto completo da fonteLiu, Peishuo, Cangqi Zhou, Xiao Liu, Jing Zhang e 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.
Texto completo da fonteZhao, Haifeng, Xiaoping Wu, Dejun Bao e 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.
Texto completo da fonteLiu, Wenya, Zhi Yang, Haitao Gan, Zhongwei Huang, Ran Zhou e 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.
Texto completo da fonteBandyopadhyay, Sambaran, Manasvi Aggarwal e 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.
Texto completo da fonteOtter, Thomas, e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Hierarchical Pooling"
Pan, Zizheng, Bohan Zhuang, Jing Liu, Haoyu He e 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.
Texto completo da fonteFernando, Basura, Peter Anderson, Marcus Hutter e 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.
Texto completo da fonteBi, Liande, Xin Sun, Fei Zhou e 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.
Texto completo da fonteAli, Waqar, Sebastiano Vascon, Thilo Stadelmann e 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.
Texto completo da fonteRoy, Kashob Kumar, Amit Roy, A. K. M. Mahbubur Rahman, M. Ashraful Amin e 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.
Texto completo da fonteSu, Zidong, Zehui Hu e 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.
Texto completo da fonteHe, Ke-Xin, Yu-Han Shen e 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.
Texto completo da fonteRachmadi, Reza Fuad, Keiichi Uchimura, Gou Koutaki e 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.
Texto completo da fonteGao, Lijian, Ling Zhou, Qirong Mao e 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.
Texto completo da fonteYu, Hualei, Yirong Yao, Jinliang Yuan e 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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