Explore the words cloud of the IDIU project. It provides you a very rough idea of what is the project "IDIU" about.
The following table provides information about the project.
Coordinator |
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Organization address contact info |
Coordinator Country | United Kingdom [UK] |
Project website | http://www.robots.ox.ac.uk/ |
Total cost | 1˙497˙271 € |
EC max contribution | 1˙497˙271 € (100%) |
Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
Code Call | ERC-2014-STG |
Funding Scheme | ERC-STG |
Starting year | 2015 |
Duration (year-month-day) | from 2015-08-01 to 2021-07-31 |
Take a look of project's partnership.
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1 | THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD | UK (OXFORD) | coordinator | 1˙497˙271.00 |
The aim of this project is to create the technology needed to understand the content of images in a detailed, human-like manner, significantly superseding the current limitations of automatic image understanding, and enabling new far reaching human-centric applications. The first goal is to substantially broaden the spectrum of visual information that machines can extract from images. For example, where current technology may discover that there is a ``person' in an image, we would like to produce a description such as ``person wearing a red uniform, tall, brown haired, with a bayonet, and a long black hat.' The second goal is to do so efficiently, by developing integrated image representations that can share knowledge and computation in multiple computer vision tasks, from detecting edges to recognising and describing thousands of different object types.
In order to do so, we will investigate, for the fist time in a systematic manner, the breadth of information that humans can extract from images, from abstract patterns to object parts and attributes, and we will incorporate it in the next generation of machine vision systems. Compared to existing technology, the new algorithms will have a significantly richer and more detailed understanding of the content of images. They will be learned from data building on recent breakthroughs in large scale discriminative and deep machine learning, and will be delivered as general-purpose open-source software for the benefit of the research community and businesses. In order to make these systems future-proof, we will develop methods to extend them automatically, by learning from images downloaded from the Internet with very little human supervision. These new advanced capabilities will be demonstrated in breakthrough applications in large scale image search and visual information retrieval.
year | authors and title | journal | last update |
---|---|---|---|
2019 |
Thomas Jakab, Andrea Vedaldi Learning Human Pose from Unaligned Data through Image Translation published pages: , ISSN: , DOI: |
2020-02-07 | |
2019 |
Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi Taking visual motion prediction to new heightfields published pages: 14-25, ISSN: 1077-3142, DOI: 10.1016/j.cviu.2019.02.005 |
Computer Vision and Image Understanding 181 | 2020-02-07 |
2019 |
X.Ji, J. F. Henriques, A. Vedaldi Invariant Information Clustering for Unsupervised Image Classification and Segmentation published pages: 9865-9874, ISSN: , DOI: |
IEEE International Conference on Computer Vision, 2019 | 2020-02-07 |
2019 |
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, Tinne Tuytelaars, Jiri Matas, Krystian Mikolajczyk HPatches: A benchmark and evaluation of handcrafted and learned local descriptors published pages: 1-1, ISSN: 0162-8828, DOI: 10.1109/tpami.2019.2915233 |
IEEE Transactions on Pattern Analysis and Machine Intelligence | 2020-02-07 |
2019 |
HAN K, VEDALDI A, ZISSERMAN A Learning to Discover Novel Visual Categories via Deep Transfer Clustering published pages: , ISSN: , DOI: |
IEEE ICCV 2019 | 2020-02-07 |
2019 |
Bertinetto L, HENRIQUES J, TORR PHS, VEDALDI A META-LEARNING WITH DIFFERENTIABLE CLOSED-FORM SOLVERS published pages: , ISSN: , DOI: |
International Conference on Learning Representations (ICLR), 2019 | 2020-02-07 |
2016 |
Aravindh Mahendran, Andrea Vedaldi Visualizing Deep Convolutional Neural Networks Using Natural Pre-images published pages: , ISSN: 0920-5691, DOI: 10.1007/s11263-016-0911-8 |
International Journal of Computer Vision | 2020-02-07 |
2018 |
J.F. Henriques, A. Vedaldi MapNet: An Allocentric Spatial memory for Mapping Environments published pages: , ISSN: , DOI: |
IEEE CVPR 2018 | 2020-02-07 |
2016 |
Novotny D, Larlus D, Vedaldi A Learning the Structure of Objects from Web Supervision published pages: , ISSN: , DOI: 10.1007/978-3-319-49409-8_19 |
ECCV Workshop on Geometry Meets Deep Learning 8-16 October | 2020-02-07 |
2018 |
Karel Lenc, Andrea Vedaldi Understanding Image Representations by Measuring Their Equivariance and Equivalence published pages: , ISSN: 0920-5691, DOI: 10.1007/s11263-018-1098-y |
International Journal of Computer Vision | 2020-02-07 |
2017 |
James Thewlis, Hakan Bilen, Andrea Vedaldi Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings published pages: , ISSN: , DOI: |
International Conference on Computer Vision (ICCV) | 2020-02-07 |
2017 |
Henriques, J.F., Vedaldi, A. Warped convolutions: efficient invariance to spatial transformation published pages: , ISSN: , DOI: |
ICML 7-9 August, 2017 | 2020-02-07 |
2016 |
Luca Bertinetto, Joao Henriques, Jack Valmadre, Philip Torr and Andrea Vedaldi Learning feed-forward one-shot learners published pages: , ISSN: , DOI: |
Neural Information Processing Systems (NIPS) 5-8 December | 2020-02-07 |
2016 |
Hakan Bilen, Andrea Vedaldi Integrated Perception with Recurrent Multi-Task Neural Networks published pages: , ISSN: , DOI: |
Neural Information Processing Systems (NIPS) | 2020-02-07 |
2018 |
R. Fong, A. Vedaldi NetVec: Quantifying and explaining how Concepts are Encoded by Filters in Deep Neural Networks published pages: , ISSN: , DOI: |
IEEE CVPR 2018 | 2020-02-07 |
2016 |
Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr Fully-Convolutional Siamese Networks for Object Tracking published pages: 850-865, ISSN: , DOI: 10.1007/978-3-319-48881-3_56 |
ECCV 2016 8-16 October 2016 | 2020-02-07 |
2017 |
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi Learning multiple visual domains with residual adapters published pages: , ISSN: , DOI: |
Neural Information Processing Systems (NIPS) | 2020-02-07 |
2016 |
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Andrea Vedaldi Deep Filter Banks for Texture Recognition, Description, and Segmentation published pages: 65-94, ISSN: 0920-5691, DOI: 10.1007/s11263-015-0872-3 |
International Journal of Computer Vision 118/1 | 2020-02-07 |
2017 |
David Novotny, Diane Larlus, Andrea Vedaldi Learning 3D Object Categories by Looking Around Them published pages: , ISSN: , DOI: |
International Conference on Computer Vision (ICCV) | 2020-02-07 |
2017 |
James Thewlis, Hakan Bilen, Andrea Vedaldi Unsupervised learning of object frames by dense equivariant image labelling published pages: , ISSN: , DOI: |
Neural Information Processing Systems (NIPS) | 2020-02-07 |
2016 |
Aravindh Mahendran, Andrea Vedaldi Salient Deconvolutional Networks published pages: 120-135, ISSN: , DOI: 10.1007/978-3-319-46466-4_8 |
ECCV 8-16 October 2016 | 2020-02-07 |
2018 |
S-A.Rebuffi, H.Bilen, A.Vedaldi Efficient parametrization of multi-domain biometric matching published pages: , ISSN: , DOI: |
IEEE CVPR 2018 | 2020-02-07 |
2016 |
Karel Lenc, Andrea Vedaldi Learning Covariant Feature Detectors published pages: 100-117, ISSN: , DOI: 10.1007/978-3-319-49409-8_11 |
ECCV Workshop on Geometry Meets Deep Learning 9 October, 2016 | 2020-02-07 |
2018 |
S. Ehrhardt; A. Monszpart; N. Mitra; A. Vedaldi Unsupervised intuitive physics from visual observations published pages: , ISSN: , DOI: |
ACCV 2018 | 2020-02-07 |
2018 |
R.Fong; A.Vedaldi Net2Vec: Quantifying and Explaining How Concepts are Encoded by Filters in Deep Neural Networks published pages: , ISSN: , DOI: |
IEEE CVPR2018 | 2020-02-07 |
2018 |
T.Jakab, A.Gupta, H.Bilen, A.Vedaldi Unsupervised Learning of Object Landmarks through Conditional Image Generation published pages: , ISSN: , DOI: |
2020-02-07 | |
2018 |
J.Hu, L.Shen, S.Albanie, G.Sum, A.Vedaldi Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks published pages: , ISSN: , DOI: |
NIPS 2018 | 2020-02-07 |
2018 |
K.Lenc, A.Vedaldi Large scale evaluation of local image feature detectors on homography datasets published pages: , ISSN: , DOI: |
2020-02-07 |
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The information about "IDIU" are provided by the European Opendata Portal: CORDIS opendata.