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pro vyhledávání: '"Shkodrani, Sindi"'
Autor:
Banerjee, Prithviraj, Shkodrani, Sindi, Moulon, Pierre, Hampali, Shreyas, Zhang, Fan, Fountain, Jade, Miller, Edward, Basol, Selen, Newcombe, Richard, Wang, Robert, Engel, Jakob Julian, Hodan, Tomas
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rig
Externí odkaz:
http://arxiv.org/abs/2406.09598
Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hierarchies in classification are far from being solved, there is a need
Externí odkaz:
http://arxiv.org/abs/2107.13627
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask prediction with limited mask labels by utilizing the more abundant weak b
Externí odkaz:
http://arxiv.org/abs/2011.11787
Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale o
Externí odkaz:
http://arxiv.org/abs/1808.00736
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision: proceedings : ICCV 2021 : 11-17 October 2021, virtual event, 2804-2813
STARTPAGE=2804;ENDPAGE=2813;TITLE=2021 IEEE/CVF International Conference on Computer Vision
STARTPAGE=2804;ENDPAGE=2813;TITLE=2021 IEEE/CVF International Conference on Computer Vision
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask prediction with limited mask labels by utilizing the more abundant weak b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e37263585cb44beab240ec5b63a7e5a3
https://doi.org/10.1109/ICCV48922.2021.00282
https://doi.org/10.1109/ICCV48922.2021.00282