Zobrazeno 1 - 10
of 273
pro vyhledávání: '"datasets and evaluation"'
Autor:
Ruta, Dan, Gilbert, Andrew, Aggarwal, Pranav, Marri, Naveen, Kale, Ajinkya, Briggs, Jo, Speed, Chris, Jin, Hailin, Faieta, Baldo, Filipkowski, Alex, Lin, Zhe, Collomosse, John
Publikováno v:
Ruta, D, Gilbert, A, Aggarwal, P, Marri, N, Kale, A, Briggs, J, Speed, C, Jin, H, Faieta, B, Filipkowski, A, Lin, Z & Collomosse, J 2022, StyleBabel : Artistic style tagging and captioning . in S Avidan, G Brostow, M Cissé & G M Farinella (eds), Computer Vision – ECCV 2022 . Lecture Notes in Computer Science, pp. 219-236, European Conference on Computer Vision 2022, Tel Aviv, Israel, 23/10/22 . https://doi.org/10.1007/978-3-031-20074-8_13
We present StyleBabel, a unique open access dataset of natural language captions and free-form tags describing the artistic style of over 135K digital artworks, collected via a novel participatory method from experts studying at specialist art and de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3094::bac60c0d43d9441109053726719c6ede
https://www.pure.ed.ac.uk/ws/files/289807467/RutaEtal2022ECCVStyleBabelArtisticStyleTagging.pdf
https://www.pure.ed.ac.uk/ws/files/289807467/RutaEtal2022ECCVStyleBabelArtisticStyleTagging.pdf
Autor:
Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcc23533216c37cf73e1c9174b18e5bb
http://arxiv.org/abs/2206.04671
http://arxiv.org/abs/2206.04671
Publikováno v:
Università degli studi di Modena e Reggio Emilia-IRIS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6e87341ec5289faaec5d5f90a7bf3c8
https://doi.org/10.1109/cvpr52688.2022.02046
https://doi.org/10.1109/cvpr52688.2022.02046
Akademický článek
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Publikováno v:
International Journal of Emerging Technologies in Learning (iJET), Vol 15, Iss 08, Pp 195-209 (2020)
Most of the educational institutes nowadays benefited from the hidden knowledge extracted from the datasets of their students, instructors and educational settings. The education system has gone through a paradigm shift from a traditional system to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35c730405a02c918b8f027bd788f9373
https://online-journals.org/index.php/i-jet/article/view/12557
https://online-journals.org/index.php/i-jet/article/view/12557
Autor:
Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, Fisher Yu
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
IEEE/CVF Conference on Computer Vision and Pattern Recognition
IEEE/CVF Conference on Computer Vision and Pattern Recognition
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7941fce1d31fbb9d3508a3cd4922496
https://hdl.handle.net/20.500.11850/583792
https://hdl.handle.net/20.500.11850/583792
Autor:
Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, Luc Van Gool
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9578afdf4459cbc36e5636f0655e753
https://hdl.handle.net/21.11116/0000-000C-1B50-C21.11116/0000-000C-1B52-A
https://hdl.handle.net/21.11116/0000-000C-1B50-C21.11116/0000-000C-1B52-A
Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a909d44a32941773b75fd93fc6357ef
http://urn.fi/urn:nbn:fi-fe202301245396
http://urn.fi/urn:nbn:fi-fe202301245396
Deep neural networks achieve outstanding results in a large variety of tasks, often outperforming human experts. However, a known limitation of current neural architectures is the poor accessibility to understand and interpret the network response to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::822c769f3c7eb18244fb34c26bc34840
https://hdl.handle.net/11572/379269
https://hdl.handle.net/11572/379269
Autor:
Papadopoulos, Dim P., Mora, Enrique, Chepurko, Nadiia, Huang, Kuan Wei, Ofli, Ferda, Torralba, Antonio
Publikováno v:
Papadopoulos, D P, Mora, E, Chepurko, N, Huang, K W, Ofli, F & Torralba, A 2022, Learning Program Representations for Food Images and Cooking Recipes . in Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . IEEE, pp. 16538-16548, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, Louisiana, United States, 19/06/2022 . https://doi.org/10.1109/CVPR52688.2022.01606
In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we propose to represent cooking recipes and food images as cooking programs.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de886ba7615f240f73031495a909c589