H2020Индивидуална стипендия2018–2022

HOL-DEEP-SENSE · Holistic Deep Modelling for User Recognition and Affective Social Behaviour Sensing

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2018-10-01 → 2022-07-01
Финансиране от ЕС: 199 828 €
Участници: 3
Схема: MSCA-IF-GF

Линиите свързват координатора с партньорите.

Накратко на български

Машинното зрение и изкуственият интелект се обучават да разпознават едновременно демографски данни, емоции и физическо състояние чрез звук, видео и биометрични сигнали. Това помага за създаването на по-естествено взаимодействие между човека и компютъра чрез социално интелигентни технологии.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Holistic Deep Modelling for User Recognition and Affective Social Behaviour Sensing

The HOL-DEEP-SENSE project aims at holistic machine perception of human charateristics such as demographic traits (age, gender), emotion, personality and physical and mental conditions. The machine learning methods developed in this project help personalize AI technologies for natural human-computer interaction. Using state-of-the-art deep learning algorithms, the project addresses the major shortcoming in today’s recognition systems that recognise affective states (e.g. emotion, sleep deprivation, depression) and other user characteristics separately. Therefore, the project seeks to understand the interrelationship between various human phenomena in order to enable human-like machine perception for emotionally and socially intelligent AI. In particular, the overarching objective of the HOL-DEEP-SENSE project is end-to-end multi-input multi-output learning, i.e. from multi-modal raw signals (audio, visual, physiological) on the front-end through hidden feature computation (in deep neural networks) to joint prediction of multiple targets (multi-task learning).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The ""Holistic Deep Modelling for User Recognition and Affective Social Behaviour Sensing"" (HOL-DEEP-SENSE) project aims at augmenting affective machines such as virtual assistants and social robots with human-like acumen based on holistic perception and understanding abilities.Social competencies comprising context awareness, salience detection and affective sensitivity present a central aspect of human communication, and thus are indispensable for enabling natural and spontaneous human-machine interaction.Therefore, with the aim to advance affective computing and social signal processing, we envision a ""Social Intelligent Multi-modal Ontological Net"" (SIMON) that builds on technologies at the leading edge of deep learning for pattern recognition.In particular, our approach is driven by multi-modal information fusion using end-to-end deep neural networks trained on large datasets, allowing SIMON to exploit combined auditory, visual and physiological analysis. In contrast to standard machine learning systems, SIMON makes use of task relatedness to adapt its topology within a novel construct of subdivided neural networks. Through deep affective feature transformation, SIMON is able to perform associative domain adaptation via transfer and multi-task learning, and thus can infer user characteristics and social cues in a holistic context.This new unified sensing architecture will enable affective computers to assimilate ontological human phenomena, leading to a step change in machine perception. This will offer a wide range of applications for health and wellbeing in future IoT-inspired environments, connected to dedicated sensors and consumer electronics.By verifying the gains through holistic sensing, the project will show the true potential of the much sought-after emotionally and socially intelligent AI, and herald a new generation of machines with hitherto unseen skills to interact with humans via universal communication channels.""

Оригинален текст от CORDIS (на английски).

Участници

TECHNISCHE UNIVERSITAET MUENCHEN · MuenchenКоординаторГермания
MASSACHUSETTS INSTITUTE OF TECHNOLOGY · CambridgeСъединени щати
UNIVERSITAET AUGSBURG · AugsburgГермания