Objective
Unsupervised visual inference can often be performed by exploiting the internal redundancy inside a single visual datum (an image or a video). The strong repetition of patches inside a single image/video provides a powerful data-specific prior for solving a variety of vision tasks in a “blind” manner: (i) Blind in the sense that sophisticated unsupervised inferences can be made with no prior examples or training; (ii) Blind in the sense that complex ill-posed Inverse-Problems can be solved, even when the forward degradation is unknown.
While the above fully unsupervised approach achieved impressive results, it relies on internal data alone, hence cannot enjoy the “wisdom of the crowd” which Deep-Learning (DL) so wisely extracts from external collections of images, yielding state-of-the-art (SOTA) results. Nevertheless, DL requires huge amounts of training data, which restricts its applicability. Moreover, some internal image-specific information, which is clearly visible, remains unexploited by today's DL methods. One such example is shown in Fig.1.
We propose to combine the power of these two complementary approaches – unsupervised Internal Data Recurrence, with Deep Learning, to obtain the best of both worlds. If successful, this will have several important outcomes including:
• A wide range of low-level & high-level inferences (image & video).
• A continuum between Internal & External training – a platform to explore theoretical and practical tradeoffs between amount of available training data and optimal Internal-vs-External training.
• Enable totally unsupervised DL when no training data are available.
• Enable supervised DL with modest amounts of training data.
• New applications, disciplines and domains, which are enabled by the unified approach.
• A platform for substantial progress in video analysis (which has been lagging behind so far due to the strong reliance on exhaustive supervised training data).
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: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences physical sciences optics microscopy super resolution microscopy
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence pattern recognition
- natural sciences computer and information sciences data science data processing
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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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-ADG - Advanced Grant
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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-2017-ADG
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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.
7610001 Rehovot
Israel
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.