Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Philip Torr"'
Autor:
Francesca Babiloni, Ioannis Marras, Jiankang Deng, Filippos Kokkinos, Matteo Maggioni, Grigorios Chrysos, Philip Torr, Stefanos Zafeiriou
Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::526c1c40e83c0036d82973213842db78
https://ora.ox.ac.uk/objects/uuid:3095e43f-79f6-42cf-a595-0093e59c9085
https://ora.ox.ac.uk/objects/uuid:3095e43f-79f6-42cf-a595-0093e59c9085
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-14
We introduce a new image segmentation task, called Entity Segmentation (ES), which aims to segment all visual entities (objects and stuffs) in an image without predicting their semantic labels. By removing the need of class label prediction, the mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::875cee8ebda6e1b89803ee702c3290fe
https://doi.org/10.1109/tpami.2022.3227513
https://doi.org/10.1109/tpami.2022.3227513
In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect network prediction. However, they usually ignore the feature hierarchies among the intermediate features. This paper introduces a h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98687e81e1378e1979c45ac0bcd19d1d
http://arxiv.org/abs/2201.09205
http://arxiv.org/abs/2201.09205
Transformers with powerful global relation modeling abilities have been introduced to fundamental computer vision tasks recently. As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classificat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58e96c6bd2033cdc953e2a10a7bd3e33
https://ora.ox.ac.uk/objects/uuid:a222054a-8742-4194-96ec-254bc9d1a85a
https://ora.ox.ac.uk/objects/uuid:a222054a-8742-4194-96ec-254bc9d1a85a
Autor:
Ziyi Shen, Qianye Yang, Yuming Shen, Francesco Giganti, Vasilis Stavrinides, Richard Fan, Caroline Moore, Mirabela Rusu, Geoffrey Sonn, Philip Torr, Dean Barratt, Yipeng Hu
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164453
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients. This paper describes a development in improving the learning-based registration algorithms, for this challenging clinical appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb6ef4eae3f5e020e4d5513042634ec4
https://doi.org/10.1007/978-3-031-16446-0_23
https://doi.org/10.1007/978-3-031-16446-0_23
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e.g., remote healthcare and affective computing). Recent deep lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80e6d96a4fab2f7ca7ff7efa98d2f491
http://arxiv.org/abs/2111.12082
http://arxiv.org/abs/2111.12082
Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16cc0bf4f6a1e5324073f944d17b25bf
http://arxiv.org/abs/2106.03149
http://arxiv.org/abs/2106.03149
Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e1c77d4df45a7d8b41077b3503ba444
Autor:
Thomas Tanay, Aivar Sootla, Matteo Maggioni, Puneet K. Dokania, Philip Torr, Ales Leonardis, Gregory Slabaugh
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and show that they tend to fail catastrophically at inference time on long v
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::791e6dfde3945c5d5c6581b19caf66da
http://arxiv.org/abs/2010.05099
http://arxiv.org/abs/2010.05099
Welcome to the 2008EuropeanConference onComputer Vision. These proce- ings are the result of a great deal of hard work by many people. To produce them, a total of 871 papers were reviewed. Forty were selected for oral pres- tation and 203 were select