Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Nogueira, Keiller"'
Autor:
Roscher, Ribana, Rußwurm, Marc, Gevaert, Caroline, Kampffmeyer, Michael, Santos, Jefersson A. dos, Vakalopoulou, Maria, Hänsch, Ronny, Hansen, Stine, Nogueira, Keiller, Prexl, Jonathan, Tuia, Devis
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on
Externí odkaz:
http://arxiv.org/abs/2312.05327
Autor:
Goncalves, Diogo Nunes, Junior, Jose Marcato, Zamboni, Pedro, Pistori, Hemerson, Li, Jonathan, Nogueira, Keiller, Goncalves, Wesley Nunes
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between
Externí odkaz:
http://arxiv.org/abs/2305.02813
Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights. These weights cannot be easily altered to incrementally learn additional classes, since the classification
Externí odkaz:
http://arxiv.org/abs/2212.00572
In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views
Externí odkaz:
http://arxiv.org/abs/2205.10592
Autor:
Machado, Gabriel, Ferreira, Edemir, Nogueira, Keiller, Oliveira, Hugo, Gama, Pedro, Santos, Jefersson A. dos
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always looking from above, some applications can benefit from complementary information provided by other perspecti
Externí odkaz:
http://arxiv.org/abs/2008.01133
Autor:
Oliveira, Hugo, Silva, Caio, Machado, Gabriel L. S., Nogueira, Keiller, Santos, Jefersson A. dos
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in a Closed Set of classes fail when new classes are found in the test phase.
Externí odkaz:
http://arxiv.org/abs/2006.14673
Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensin
Externí odkaz:
http://arxiv.org/abs/2001.10063
The recent impressive results of deep learning-based methods on computer vision applications brought fresh air to the research and industrial community. This success is mainly due to the process that allows those methods to learn data-driven features
Externí odkaz:
http://arxiv.org/abs/1906.01751
Autor:
Nogueira, Keiller, Santos, Jefersson A. dos, Menini, Nathalia, Silva, Thiago S. F., Morellato, Leonor Patricia C., Torres, Ricardo da S.
Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating an
Externí odkaz:
http://arxiv.org/abs/1903.00774
Autor:
Souza, Anderson P., Oliveira, Bruno A., Andrade, Mauren L., Starling, Maria Clara V.M., Pereira, Alexandre H., Maillard, Philippe, Nogueira, Keiller, dos Santos, Jefersson A., Amorim, Camila C.
Publikováno v:
In Science of the Total Environment July 2023