Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Viorica Patraucean"'
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012243
ECCV (4)
ECCV (4)
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles. Leveraging operation pipelining and multi-rate clocks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b4f398bcbd4707aa130d3f509b61a5e
https://ora.ox.ac.uk/objects/uuid:09a63482-0b82-4764-a999-38158a8be875
https://ora.ox.ac.uk/objects/uuid:09a63482-0b82-4764-a999-38158a8be875
Autor:
Viorica Patraucean, Dimitrios Vytiniotis, Joao Carreira, Mateusz Malinowski, Grzegorz Swirszcz
Publikováno v:
CVPR
How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the forward and backward passes are completed. For temporal signals, this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::327cf37390bdc13afe053417d000661b
Publikováno v:
CVPR
We propose Sideways, an approximate backpropagation scheme for training video models. In standard backpropagation, the gradients and activations at every computation step through the model are temporally synchronized. The forward activations need to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d012ecd389ef011d031662789568a8e
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 39:788-802
We propose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ddf8261725e203c7b2c9fa97a3a69a5f
https://doi.org/10.1109/tpami.2016.2558150
https://doi.org/10.1109/tpami.2016.2558150
Autor:
Ioannis Brilakis, Viorica Patraucean, Jamie Yeung, Mohammad Nahangi, Iro Armeni, Carl T. Haas
Publikováno v:
Advanced Engineering Informatics. 29(2):162-171
Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15e2041b8e82c77cd10fcb5bec4c0ed9
Publikováno v:
CVPR
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2570c2010db9420fb639b32f639c72d
https://doi.org/10.1109/cvpr.2016.442
https://doi.org/10.1109/cvpr.2016.442
Publikováno v:
Construction Research Congress 2016.
This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.229
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::196bae8c5093e0b3036f2dd0cef4a0ff
https://doi.org/10.1061/9780784479827.229
https://doi.org/10.1061/9780784479827.229
Publikováno v:
ICRA
We introduce SceneNet, a framework for generating high-quality annotated 3D scenes to aid indoor scene understanding. SceneNet leverages manually-annotated datasets of real world scenes such as NYUv2 to learn statistics about object co-occurrences an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3eee8e7f12cb2f8155b9b2787887d448
https://doi.org/10.1109/icra.2016.7487797
https://doi.org/10.1109/icra.2016.7487797
Autor:
Viorica Patraucean, John McCormac, Andrew J. Davison, Michael Bloesch, Simon Stent, Ankur Handa
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319494081
ECCV Workshops (3)
14th European Conference on Computer Vision (ECCV)
ECCV Workshops (3)
14th European Conference on Computer Vision (ECCV)
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::684b9881a758caf3a03017c66e4b1db3
https://doi.org/10.1007/978-3-319-49409-8_9
https://doi.org/10.1007/978-3-319-49409-8_9
Publikováno v:
ICIP
We describe a simple metric for image patches similarity, together with a robust criterion for unsupervised patch matching. The gradient orientations at corresponding positions in the two patches are compared and the normalized errors are accumulated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f879f410633c64ac4e72f8a1bea714a
https://doi.org/10.1109/icip.2015.7350939
https://doi.org/10.1109/icip.2015.7350939