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.

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

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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