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Academic literature on the topic 'Hierarchical Pooling'

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Published: 25 May 2024

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Journal articles on the topic "Hierarchical Pooling"

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Fernando, Basura, and Stephen Gould. "Discriminatively Learned Hierarchical Rank Pooling Networks." International Journal of Computer Vision 124, no. 3 (June 24, 2017): 335–55. http://dx.doi.org/10.1007/s11263-017-1030-x.

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Ranjan, Ekagra, Soumya Sanyal, and Partha Talukdar. "ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5470–77. http://dx.doi.org/10.1609/aaai.v34i04.5997.

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Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures. ASAP utilizes a novel self-attention network along with a modified GNN formulation to capture the importance of each node in a given graph. It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph. Through extensive experiments on multiple datasets and theoretical analysis, we motivate our choice of the components used in ASAP. Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks. ASAP has an average improvement of 4%, compared to current sparse hierarchical state-of-the-art method. We make the source code of ASAP available to encourage reproducible research 1.
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Chen, Jiawang, and Zhenqiang Wu. "Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling." Applied Sciences 12, no. 19 (September 28, 2022): 9795. http://dx.doi.org/10.3390/app12199795.

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Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction. However, most signed network embedding approaches neglect hierarchical graph pooling in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, this paper presents a unique deep learning-based Signed network embedding model with Hierarchical Graph Pooling (SHGP). To be more explicit, a hierarchical pooling mechanism has been developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of the nodes. Extensive experiments on three large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.
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Grumitt, R. D. P., Luke R. P. Jew, and C. Dickinson. "Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler." Monthly Notices of the Royal Astronomical Society 496, no. 4 (June 26, 2020): 4383–401. http://dx.doi.org/10.1093/mnras/staa1857.

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ABSTRACT In this paper, we present a novel implementation of Bayesian cosmic microwave background (CMB) component separation. We sample from the full posterior distribution using the No-U-Turn Sampler (NUTS), a gradient-based sampling algorithm. Alongside this, we introduce new foreground modelling approaches. We use the mean shift algorithm to define regions on the sky, clustering according to naively estimated foreground spectral parameters. Over these regions we adopt a complete pooling model, where we assume constant spectral parameters, and a hierarchical model, where we model individual pixel spectral parameters as being drawn from underlying hyperdistributions. We validate the algorithm against simulations of the LiteBIRD and C-Band All-Sky Survey (C-BASS) experiments, with an input tensor-to-scalar ratio of r = 5 × 10−3. Considering multipoles 30 ≤ ℓ < 180, we are able to recover estimates for r. With LiteBIRD-only observations, and using the complete pooling model, we recover r = (12.9 ± 1.4) × 10−3. For C-BASS and LiteBIRD observations we find r = (9.0 ± 1.1) × 10−3 using the complete pooling model, and r = (5.2 ± 1.0) × 10−3 using the hierarchical model. Unlike the complete pooling model, the hierarchical model captures pixel-scale spatial variations in the foreground spectral parameters, and therefore produces cosmological parameter estimates with reduced bias, without inflating their uncertainties. Measured by the rate of effective sample generation, NUTS offers performance improvements of ∼103 over using Metropolis–Hastings to fit the complete pooling model. The efficiency of NUTS allows us to fit the more sophisticated hierarchical foreground model that would likely be intractable with non-gradient-based sampling algorithms.
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Devineni, Naresh, Upmanu Lall, Neil Pederson, and Edward Cook. "A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression." Journal of Climate 26, no. 12 (June 15, 2013): 4357–74. http://dx.doi.org/10.1175/jcli-d-11-00675.1.

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Abstract A hierarchical Bayesian regression model is presented for reconstructing the average summer streamflow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamflow series considering parameter uncertainty. The vectors of regression coefficients are modeled as draws from a common multivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure. The covariance structure of the streamflow residuals across sites is explicitly modeled. The resulting partial pooling of information across multiple stations leads to a reduction in parameter uncertainty. The effect of no pooling and full pooling of station information, as end points of the method, is explored. The no-pooling model considers independent estimation of the regression coefficients for each streamflow gauge with respect to each tree-ring chronology. The full-pooling model considers that the same regression coefficients apply across all streamflow sites for a particular tree-ring chronology. The cross-site correlation of residuals is modeled in all cases. Performance on metrics typically used by tree-ring reconstruction experts, such as reduction of error, coefficient of efficiency, and coverage rates under credible intervals is comparable to, or better, for the partial-pooling model relative to the no-pooling model, and streamflow estimation uncertainty is reduced. Long record simulations from reconstructions are used to develop estimates of the probability of duration and severity of droughts in the region. Analysis of monotonic trends in the reconstructed drought events do not reject the null hypothesis of no trend at the 90% significance over 1754–2000.
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Chen, Junying, and Ying Chen. "Saliency Enhanced Hierarchical Bilinear Pooling for Fine-Grained Classification." Journal of Computer-Aided Design & Computer Graphics 33, no. 2 (February 1, 2021): 241–49. http://dx.doi.org/10.3724/sp.j.1089.2021.18399.

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Sanchez-Giraldo, Luis G., Md Nasir Uddin Laskar, and 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.

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Tan, Min, Fu Yuan, Jun Yu, Guijun Wang, and Xiaoling Gu. "Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1s (February 28, 2022): 1–23. http://dx.doi.org/10.1145/3492221.

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How to extract distinctive features greatly challenges the fine-grained image classification tasks. In previous models, bilinear pooling has been frequently adopted to address this problem. However, most bilinear pooling models neglect either intra or inter layer feature interaction. This insufficient interaction brings in the loss of discriminative information. In this article, we devise a novel fine-grained image classification approach named M ulti-scale S elective H ierarchical bi Q uadratic P ooling (MSHQP). The proposed biquadratic pooling simultaneously models intra and inter layer feature interactions and enhances part response by integrating multi-layer features. The subsequent coarse-to-fine multi-scale interaction structure captures the complementary information within features. Finally, the active interaction selection module adaptively learns the optimal interaction subset for a specific dataset. Consequently, we obtain a robust image representation with coarse-to-fine semantics. We conduct experiments on five benchmark datasets. The experimental results demonstrate that MSHQP achieves competitive or even match the state-of-the-art methods in terms of both accuracy and computational efficiency, with 89.0%, 94.9%, 93.4%, 90.4%, and 91.5% top-1 classification accuracy on CUB-200-2011, Stanford-Cars, FGVC-Aircraft, Stanford-Dog, and VegFru, respectively.
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Ko, Sung Moon, Sungjun Cho, Dae-Woong Jeong, Sehui Han, Moontae Lee, and Honglak Lee. "Grouping Matrix Based Graph Pooling with Adaptive Number of Clusters." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8334–42. http://dx.doi.org/10.1609/aaai.v37i7.26005.

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Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assuming that all input graphs share the same number of clusters. In inductive settings where the number of clusters could vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.
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Li, 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.

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Dissertations / Theses on the topic "Hierarchical Pooling"

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Mazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.

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De nos jours, les contenus vidéos sont omniprésents grâce à Internet et les smartphones, ainsi que les médias sociaux. De nombreuses applications de la vie quotidienne, telles que la vidéo surveillance et la description de contenus vidéos, ainsi que la compréhension de scènes visuelles, nécessitent des technologies sophistiquées pour traiter les données vidéos. Il devient nécessaire de développer des moyens automatiques pour analyser et interpréter la grande quantité de données vidéo disponibles. Dans cette thèse, nous nous intéressons à la reconnaissance d'actions dans les vidéos, c.a.d au problème de l'attribution de catégories d'actions aux séquences vidéos. Cela peut être considéré comme un ingrédient clé pour construire la prochaine génération de systèmes visuels. Nous l'abordons avec des méthodes d'intelligence artificielle, sous le paradigme de l'apprentissage automatique et de l'apprentissage profond, notamment les réseaux de neurones convolutifs. Les réseaux de neurones convolutifs actuels sont de plus en plus profonds, plus gourmands en données et leur succès est donc tributaire de l'abondance de données d'entraînement étiquetées. Les réseaux de neurones convolutifs s'appuient également sur le pooling qui réduit la dimensionnalité des couches de sortie (et donc atténue leur sensibilité à la disponibilité de données étiquetées)
Nowadays, 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
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Book chapters on the topic "Hierarchical Pooling"

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Zhang, Can, Yuexian Zou, and 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.

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Yu, Chaojian, Xinyi Zhao, Qi Zheng, Peng Zhang, and 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.

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Liu, Yan, Zhi Liu, and 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.

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Thornton, John, Jolon Faichney, Michael Blumenstein, and 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.

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Fei, Xiaohan, Konstantine Tsotsos, and 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.

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Liu, Peishuo, Cangqi Zhou, Xiao Liu, Jing Zhang, and 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.

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Zhao, Haifeng, Xiaoping Wu, Dejun Bao, and 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.

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Liu, Wenya, Zhi Yang, Haitao Gan, Zhongwei Huang, Ran Zhou, and 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.

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Bandyopadhyay, Sambaran, Manasvi Aggarwal, and 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.

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Otter, Thomas, and 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.

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Conference papers on the topic "Hierarchical Pooling"

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Pan, Zizheng, Bohan Zhuang, Jing Liu, Haoyu He, and 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.

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Fernando, Basura, Peter Anderson, Marcus Hutter, and 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.

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Bi, Liande, Xin Sun, Fei Zhou, and 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.

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Ali, Waqar, Sebastiano Vascon, Thilo Stadelmann, and 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.

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Roy, Kashob Kumar, Amit Roy, A. K. M. Mahbubur Rahman, M. Ashraful Amin, and 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.

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Su, Zidong, Zehui Hu, and 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.

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He, Ke-Xin, Yu-Han Shen, and 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.

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Rachmadi, Reza Fuad, Keiichi Uchimura, Gou Koutaki, and 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.

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Gao, Lijian, Ling Zhou, Qirong Mao, and 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.

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Yu, Hualei, Yirong Yao, Jinliang Yuan, and 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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