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Deep Ensemble Learning Model Based on Covariance Pooling of Multi-Layer CNN Features.

, , , , , and . ICIP, page 1081-1085. IEEE, (2022)

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Ensemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification. (Méthodes d'ensemble sur l'espace des matrices de covariance : application à la classification de scènes de télédétection et de séries temporelles multivariées).. University of Bordeaux, France, (2021)Cluster Kernel For Learning Similarities Between Symmetric Positive Definite Matrix Time Series., , , and . ICIP, page 3304-3308. IEEE, (2020)Ensemble Learning Approaches Based on Covariance Pooling of CNN Features for High Resolution Remote Sensing Scene Classification., , , , and . Remote. Sens., 12 (20): 3292 (2020)AI4GEO: LOD0 Generation for 3D building models., , , , and . JURSE, page 1-4. IEEE, (2023)Deep Ensemble Learning Model Based on Covariance Pooling of Multi-Layer CNN Features., , , , , and . ICIP, page 1081-1085. IEEE, (2022)A Gaussian mixture model with multiple tangent planes., , , and . EUSIPCO, page 950-954. IEEE, (2023)Image classification based on log-Euclidean Fisher Vectors for covariance matrix descriptors., , , , and . IPTA, page 1-6. IEEE, (2018)An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features., , , , , and . EUSIPCO, page 1-5. IEEE, (2019)Remote Sensing Scene Classification Based on Covariance Pooling of Multi-layer CNN Features Guided by Saliency Maps., , , and . ICPRAI (1), volume 13363 of Lecture Notes in Computer Science, page 579-590. Springer, (2022)