Opendata, web and dolomites


Deeply Explainable Intelligent Machines

Total Cost €


EC-Contrib. €






 DEXIM project word cloud

Explore the words cloud of the DEXIM project. It provides you a very rough idea of what is the project "DEXIM" about.

multiple    valuable    frequently    data    continuity    humans    adaptive    machine    easily    world    scene    stable    language    interactions    adapt    hurricane    fail    mobile    warn    ultimately    explanations    explanatory    scaffold    practical    gdpr    explainable    possibility    incorporate    output    industry    abnormal    point    fashioned    strengthen    hence    positive    ailment    patients    save    opaque    natural    memory    direct    critical    law    robotics    market    unable    learning    attain    artificially    justifying    environment    supports    millions    makers    wrong    modalities    transparent    imagery    monitor    maker    vehicles    mechanisms    monitoring    led    lives    competitive    self    domain    human    visual    building    put    automatic    medical    collaborate    concerning    justify    driving    intelligent    temporal    arise    decisions    trust    satellite    constraints    situations    contrast    decision    position    explanation    trainable    notably    operate    prevent    deep    themselves   

Project "DEXIM" data sheet

The following table provides information about the project.


Organization address
postcode: 72074

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Total cost 1˙500˙000 €
 EC max contribution 1˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-STG
 Funding Scheme ERC-STG
 Starting year 2019
 Duration (year-month-day) from 2019-12-01   to  2024-11-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Explanations are valuable because they scaffold the kind of learning that supports adaptive behaviour, e.g. explanations enable users to adapt themselves to the situations that are about to arise. Explanations allow us to attain a stable environment and have the possibility to control it, e.g. explanations put us in a better position to control the future. Explanations in the medical domain can help patients identify and monitor the abnormal behaviour of their ailment. In the domain of self-driving vehicles they can warn the user of some critical state and collaborate with her to prevent a wrong decision. In the domain of satellite imagery, an explanatory monitoring system justifying the evidence of a future hurricane can save millions of lives. Hence, a learning machine that a user can trust and easily operate need to be fashioned with the ability of explanation. Moreover, according to GDPR, an automatic decision maker is required to be transparent by law.

As decision makers, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions. In contrast, artificially intelligent systems are frequently seen as opaque and are unable to explain their decisions. This is particularly concerning as ultimately such systems fail in building trust with human users.

In this proposal, the goal is to build a fully transparent end-to-end trainable and explainable deep learning approach for visual scene understanding. To achieve this goal, we will make use of the positive interactions between multiple data modalities, incorporate uncertainty and temporal continuity constraints, as well as memory mechanisms. The output of this proposal will have direct consequences for many practical applications, most notably in mobile robotics and intelligent vehicles industry. This project will therefore strengthen the user’s trust in a very competitive market.

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The information about "DEXIM" are provided by the European Opendata Portal: CORDIS opendata.

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