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Expectational Visual Artificial Intelligence

Project description

Creating the next generation of insightful visual algorithms

The next generation of visual algorithms are required to expect what happens next given past visual observations stored as data. Visual artificial intelligence must also be able to prevent before the fact rather than explain after it. The EU-funded EVA project aims to design algorithms that learn to expect possible futures from visual sequences. The main challenge in this is having visual algorithms that learn temporality in visual sequences. EVA will address fundamental research issues in the automatic interpretation of future visual sequences. The project's results will serve as a basis for ground-breaking technological advances in practical vision applications.

Objective

Visual artificial intelligence automatically interprets what happens in visual data like videos. Todays research strives with queries like: Is this person playing basketball?; Find the location of the brain stroke; or Track the glacier fractures in satellite footage. All these queries are about visual observations already taken place. Todays algorithms focus on explaining past visual observations. Naturally, not all queries are about the past: Will this person draw something in or out of their pocket?; Where will the tumour be in 5 seconds given breathing patterns and moving organs?; or How will the glacier fracture given the current motion and melting patterns?. For these queries and all others, the next generation of visual algorithms must expect what happens next given past visual observations. Visual artificial intelligence must also be able to prevent before the fact, rather than explain only after it. I propose an ambitious 5-year project to design algorithms that learn to expect the possible futures from visual sequences.

The main challenge for expecting possible futures is having visual algorithms that learn temporality in visual sequences. Todays algorithms cannot do this convincingly. First, they are time-deterministic and ignore uncertainty, part of any expected future. I propose time-stochastic visual algorithms. Second, todays algorithms are time-extrinsic and treat time as an external input or output variable. I propose time-intrinsic visual algorithms that integrate time within their latent representations. Third, visual algorithms must account for all innumerable spatiotemporal dynamics, despite their finite nature. I propose time-geometric visual algorithms that constrain temporal latent spaces to known geometries.

EVA addresses fundamental research issues in the automatic interpretation of future visual sequences. Its results will serve as a basis for ground-breaking technological advances in practical vision applications.

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Keywords

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Programme(s)

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Topic(s)

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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.

ERC-STG - Starting Grant

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Call for proposal

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(opens in new window) ERC-2020-STG

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Host institution

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution

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.

€ 1 499 562,00
Total cost

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.

€ 1 499 562,00

Beneficiaries (1)

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