Objective
Computer vision has gained considerable momentum in recent years – both in industry and academia. There seems to be a spirit that the time is ripe to realize grand goals and to bring computer vision from the lab into real life. But is a vision system already as good as a human is? The answer is: “Unfortunately, not yet.” Given a single image, a child can describe the objects and their relationships in a much more detailed manner than any computer can. Also, humans can quite effortlessly “visually extract” an object from its background, even in the presence of fine details such as hair. Computers cannot yet achieve this automatically. But, for many real-world applications it is absolutely necessary to reach such levels of rich output, accuracy, quality, robustness, and system autonomy. In this proposal we try to get closer to this overarching goal. We believe that the key to success is a richer representation. Here “rich” stands for rich, detailed output, modelling rich, physical and semantic constraints, and learning rich, statistical relations between different aspects of a scene. Towards this end we propose the Rich Scene Model (RSM), which is one joint statistical, structured model of many physical and semantic scene aspects that can take full advantage of the synergy effect between all its components. This effort goes beyond previous attempts, in many respects. However, it is simple to say “We will build the best ever joint, rich scene model”. Accordingly, the crux of this proposal is to design novel models, learning and inference techniques to make the RSM a reality. This proposal addresses not only theoretical questions such as, “What can we infer from a few images of a dynamically changing 3D scene?”, and “Is our RSM rich enough to make statistical learning “work better” than deterministic learning?” we also propose a model that can give new forms of output, better deal with challenging real world scenarios, and can adapt nicely to human and application needs
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences physical sciences astronomy astrophysics
- social sciences sociology governance crisis management
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-COG - Consolidator Grant
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2014-CoG
See all projects funded under this callHost institution
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
69117 Heidelberg
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.