Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Doll��r, Piotr"'
This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. Firs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e25910dc7e272692fb236f391d6100a7
http://arxiv.org/abs/2111.06377
http://arxiv.org/abs/2111.06377
Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::874d4dd28ab875625f77e5cf18c272ee
http://arxiv.org/abs/2106.14881
http://arxiv.org/abs/2106.14881
Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels. In this paper, we ask the question: can we find high-quality neural architect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72ba145b405fc56445ef266f84d2e40e
http://arxiv.org/abs/2003.12056
http://arxiv.org/abs/2003.12056
Autor:
Doll��r, Piotr, Zitnick, C. Lawrence
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take ad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88fcb45319aa0f2f59ca065abda7314f
http://arxiv.org/abs/1406.5549
http://arxiv.org/abs/1406.5549
Autor:
Lin, Tsung-Yi, Maire, Michael, Belongie, Serge, Bourdev, Lubomir, Girshick, Ross, Hays, James, Perona, Pietro, Ramanan, Deva, Zitnick, C. Lawrence, Doll��r, Piotr
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of comple
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::614188db39ac4c4183fa77fdf53b032c
http://arxiv.org/abs/1405.0312
http://arxiv.org/abs/1405.0312
Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddf2b66f2adf524d70644412a18bd1f1
https://infoscience.epfl.ch/record/224543
https://infoscience.epfl.ch/record/224543