Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Rezende, Rafael A."'
Autor:
Kalantidis, Yannis, Sarıyıldız, Mert Bülent, Rezende, Rafael S., Weinzaepfel, Philippe, Larlus, Diane, Csurka, Gabriela
State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the
Externí odkaz:
http://arxiv.org/abs/2402.09237
Autor:
Yang, Jheng-Hong, Lassance, Carlos, de Rezende, Rafael Sampaio, Srinivasan, Krishna, Redi, Miriam, Clinchant, Stéphane, Lin, Jimmy
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained transformers have led to significant improvements in retrieval effecti
Externí odkaz:
http://arxiv.org/abs/2304.01961
Autor:
Rezende, Rafael Marins1 (AUTHOR) faelfisio@yahoo.com.br, Coimbra, Roney Santos2 (AUTHOR) roney.coimbra@fiocruz.br, Kohlhoff, Markus2 (AUTHOR) markus.kohlhoff@fiocruz.br, Favarato, Lukiya Silva Campos3 (AUTHOR) lscampos@ufv.br, Martino, Hércia Stampini Duarte4 (AUTHOR) hercia@ufv.br, Leite, Luciano Bernardes5,6 (AUTHOR) luciano.leite@ufv.br, Soares, Leoncio Lopes5 (AUTHOR) leoncio.lopes@ufv.br, Encarnação, Samuel7,8,9 (AUTHOR) samuel01.encarnacao@gmail.com, Forte, Pedro6,9,10,11 (AUTHOR) mmonteiro@ipb.pt, de Barros Monteiro, António Miguel6,9 (AUTHOR), Peluzio, Maria do Carmo Gouveia4 (AUTHOR) mpeluzio@ufv.br, José Natali, Antônio5 (AUTHOR) anatali@ufv.br
Publikováno v:
Cells (2073-4409). Oct2024, Vol. 13 Issue 19, p1647. 11p.
Autor:
Moreira, Thais G., Cox, Laura M., Da Silva, Patrick, Mangani, Davide, De Oliveira, Marilia G., Escobar, Giulia, Lanser, Toby B., Murphy, Liam, Lobo, Eduardo.L.C., Milstein, Omer, Gauthier, Christian D., Clara Guimarāes, Ana, Schwerdtfeger, Luke, Ekwudo, Mellicient N., Wasén, Caroline, Liu, Shirong, Menezes, Gustavo B., Ferreira, Enio, Gabriely, Galina, Anderson, Ana C., Faria, Ana Maria C., Rezende, Rafael M., Weiner, Howard L.
Publikováno v:
In Mucosal Immunology October 2024 17(5):911-922
An intuitive way to search for images is to use queries composed of an example image and a complementary text. While the first provides rich and implicit context for the search, the latter explicitly calls for new traits, or specifies how some elemen
Externí odkaz:
http://arxiv.org/abs/2203.08101
Contrastive losses have long been a key ingredient of deep metric learning and are now becoming more popular due to the success of self-supervised learning. Recent research has shown the benefit of decomposing such losses into two sub-losses which ac
Externí odkaz:
http://arxiv.org/abs/2112.11743
Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propose a noise-resistant method for image retrieval named Teacher-based Se
Externí odkaz:
http://arxiv.org/abs/2112.10453
We present our solution for the M5 Forecasting - Uncertainty competition. Our solution ranked 6\ts{th} out of 909 submissions across all hierarchical levels and ranked first for prediction at the finest level of granularity (product-store sales, i.e.
Externí odkaz:
http://arxiv.org/abs/2111.14721
Autor:
Campuzano, Alberto Jorge Baeza, Rezende, Rafael Barbosa, Taborda, Nestor Cifuentes, dos Santos, Júlio Cesar, Pereira, Fabiano Vargas, Panzera, Tulio Hallak
Publikováno v:
In Materials Today Communications August 2024 40
Autor:
Chun, Sanghyuk, Oh, Seong Joon, de Rezende, Rafael Sampaio, Kalantidis, Yannis, Larlus, Diane
Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particul
Externí odkaz:
http://arxiv.org/abs/2101.05068