Partenariat humain-IA
Le programme de recherche Partenariat Humain-IA explore comment les humains et l’intelligence artificielle peuvent collaborer pour améliorer la créativité, la prise de décision et faciliter les tâches quotidiennes. Nos recherches se concentrent sur la compréhension des similitudes et des différences entre les humains et les machines. Elles impliquent également le développement d’interfaces multimodales et multilingues intégrant la parole, les comportements de communication non verbale et l’haptique, afin de permettre une interaction humain-IA intuitive. Enfin, nous travaillons à améliorer la fiabilité de l’IA grâce aux retours des utilisateurs et à des interactions enrichies.
Ancré dans l’expertise d’Idiap en interaction multimodale, en systèmes cognitifs et en collaboration humain-robot, notre travail vise à créer une IA qui fonctionne non seulement pour les personnes, mais aussi avec elles, afin d’amplifier le potentiel humain et l’impact sociétal. Plus précisément, nous visons à concevoir des systèmes d’IA qui améliorent l’accès à la connaissance et offrent un soutien adaptatif et personnalisé dans des rôles allant des assistants personnels aux compagnons industriels et médicaux.
Ces systèmes assistent également les personnes dans leur travail et leur vie quotidienne grâce aux technologies robotiques.
Domaines d’application
- Éducation & Technologies d’apprentissage
- Robotique & Automatisation
- Technologies d’assistance
- Fabrication & Ingénierie
Domaines d'expertise
- Bio-informatique & Informatique de la santé
- Science des données & Informatique sociale
- Interaction homme–machine
- Imagerie & Vision par ordinateur
- Apprentissage automatique
- Traitement automatique du langage naturel
- Robotique & Systèmes autonomes
- Sécurité & Protection de la vie privée
- Traitement du signal
- Traitement de la parole & du signal audio
Ce programme contribue aux ODD des Nations-Unies suivants
Personnes
ABBET, Philip
(Senior Research and Development Engineer)
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AGRAWAL, Sanat (Kumar)
(Research Intern)
ALONSO DEL BARRIO, David
(Research and Development Engineer)
BAKKER, Saray
(Research Intern)
BECKMANN, Pierre
(Doctoral Researcher)
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BEN MAHMOUD, Imen
(Research and Development Engineer)
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BILALOGLU, Cem
(Doctoral Researcher)
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BORNET, Olivier
(Head of Research and Development Team)
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BURDISSO, Sergio (Gastón)
(Research Associate)
CALINON, Sylvain
(Senior Research Scientist)
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CANÉVET, Olivier
(Senior Research and Development Engineer)
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CARRON, Daniel
(Senior Research and Development Engineer)
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CATIC, Hana
(Research Intern)
CLIVAZ, Guillaume
(Senior Research and Development Engineer)
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COMAN, Andrei (Catalin)
(Doctoral Researcher)
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DARWICHE, Nael
(Research Intern)
DAYER, Yannick (Nicolas)
(Senior Research and Development Engineer)
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DELMAS, Maxime
(Postdoctoral Researcher)
DROZ, William
(Senior Research and Development Engineer)
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EL HAJAL, Karl
(Doctoral Researcher)
EL ZEIN, Dina
(Doctoral Researcher)
ESMAEILY, Abolghasem
(Research Intern)
ESPINOSA MENA, Jose Rafael
(Doctoral Researcher)
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FREITAS, André
(Research Scientist)
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GAIST, Samuel
(Senior Research and Development Engineer)
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GEISSBUHLER, David
(Research Associate)
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GUPTA, Anshul
(Doctoral Researcher)
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GUPTE, Vivek (Samir)
(Doctoral Researcher)
HAIDAR, Nawal
(Doctoral Researcher)
HENDERSON, James (Brinton)
(Senior Research Scientist)
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HERMANN, Enno
(Postdoctoral Researcher)
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HERMUS, James (Russell)
(Postdoctoral Researcher)
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HOVSEPYAN, Sevada
(Research Associate)
JIANG, Liangze
(Doctoral Researcher)
JULLIEN, Maël
(Research Intern)
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JUNG, Vincent
(Doctoral Researcher)
KAYAL, Salim
(Senior Research and Development Engineer)
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KHALIL, Driss
(Junior R&D / Research Assistant)
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KULKARNI, Ajinkya (Vijay)
(Postdoctoral Researcher)
KUMAR, Shashi
(Doctoral Researcher)
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LANFRANCONI, Michele
(Research Intern)
LI DONG, Virgílio
(Apprentice)
LI, Yiming
(Doctoral Researcher)
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LÖW, Tobias
(Postdoctoral Researcher)
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LUO, Yongkang
(Academic Visitor)
MACEIRAS, Jérémy
(Senior Research and Development Engineer)
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MAGIMAI DOSS, Mathew
(Senior Research Scientist)
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MARCEL, Christine
(Senior Research and Development Engineer)
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MARIĆ, Ante
(Doctoral Researcher)
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MAYORAZ, André
(Research and Development Engineer)
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MICHEL, Samuel
(Research and Development Engineer)
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MOHAMMADI, Amir
(Research and Development Engineer)
MOHR, Isabelle
(Doctoral Researcher)
MOTLICEK, Petr
(Senior Research Scientist)
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MUKHERJEE, Anirban
(Doctoral Researcher)
NANCHEN, Alexandre
(Senior Research and Development Engineer)
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ODOBEZ, Jean-Marc
(Senior Research Scientist with Academic Title)
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PRASAD, Amrutha
(Postdoctoral Researcher)
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PUROHIT, Tilak
(Research Intern)
RAJAPAKSHE, Shalutha
(Doctoral Researcher)
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RAZMJOO FARD, Amirreza
(Doctoral Researcher)
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ROUX, Thibault
(Postdoctoral Researcher)
SANCHEZ-CORTES, Dairazalia
(Postdoctoral Researcher)
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SCHONGER, Martin
(Doctoral Researcher)
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SENFT, Emmanuel
(Research Scientist)
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SHIRAKAMI, Haruki
(Doctoral Researcher)
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SPAHR, Guillaume
(Research Intern)
TAFASCA, Samy
(Doctoral Researcher)
TENEY, Damien
(Research Scientist)
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TIMONINA-FARKAS, Anna
(Research Associate)
VERZAT, Colombine
(Senior Research and Development Engineer)
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VILLAMIZAR, Michael (Alejandro)
(Research Associate)
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VILLATORO TELLO, Esaú
(Research Associate)
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VUILLECARD, Pierre
(Doctoral Researcher)
WANG, Hengfei
(Postdoctoral Researcher)
WATAWANA, Hasindri (Sankalpana)
(Doctoral Researcher)
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WYSOCKI, Oskar
(Postdoctoral Researcher)
XU, Lei
(Doctoral Researcher)
ZANGGER, Alicia
(Research and Development Engineer)
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ZHANG, Yan
(Doctoral Researcher)
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Publications choisies
Neural Network Adaptation and Data Augmentation for MultiSpeaker Direction-of-Arrival Estimation, W. He, P. Motlicek and J.-M. Odobez, IEEE/ACM Trans. on Audio, Speech and Language Processing, 29, pp. 1303-1317, 2021.
The first viable deep learning framework (task definition, network architecture, training paradigm) for solving fundamental auditory tasks such as sound source localization, speaker identification and speech/non-speech classification. The framework is suitable for highly noisy environments and overcomes limitations of previous methods, which heavily relied on idealized sound and environment models and are inadequate for everyday situations with multiple sound sources, background noise, short utterances, and lack of prior knowledge on the number of sound sources. The method learns sound source localization models with limited training resources leveraging simulated and weakly-labeled real audio data.
Active Learning by Feature Mixing, A. Parvaneh, E. Abbasnejad, D. Teney, G. R. Haffari, A. Van Den Hengel, & J. Q. Shi, In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 12227-12236, 2022.
A method to train deep learning models with humans in the loop. Current approaches to machine learning depend on large amounts of data that are costly or difficult to acquire. This paper presents an active learning approach where human experts interact with the learning algorithm to iteratively refine and resolve inconsistencies in a model by labeling a small set of training examples. This approach contributes to widening the accessibility of machine learning technologies to small organizations.
Learning Joint Space Reference Manifold for Reliable Physical Assistance, Razmjoo, A., Brecelj, T., Savevska, K., Ude, A., Petric, T. and Calinon, S., In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS), 2023.
Projets choisis
C-LING, 2022-2026, SNSF, Van der Plas: TOWARDS CREATIVE SYSTEMS WITH LINGUISTIC MODELLING
This project aims to investigate what aspects computational models need to perform creative cognitive tasks, from generating relatively simple novel concepts to more complex and structured ideas, across multiple domains and languages. More in particular, it aims to answer what types of structured and unstructured knowledge are needed and what models best integrate these types of knowledge.
NeuMath, 2022-2024, SNSF, Freitas: NEUMATH: NEURAL DISCOURSE INFERENCE OVER MATHEMATICAL TEXTS
NeuMath will develop models which can jointly represent and reason over two symbolic modalities (natural language and mathematical expressions) and will build the foundations to deliver embedding models which can interpret and support the generation of mathematical arguments (by leveraging available large-scale scientific corpora).
SMILE-II, 2021-2024, SNSF Sinergia, Magimai Doss: SMILE-II SCALABLE MULTIMODAL SIGN LANGUAGE TECHNOLOGY FOR SIGN LANGUAGE LEARNING AND ASSESSMENT PHASE-II
The proposed project SMILE-II aims to research and build advanced technology for sign language learning. More precisely, the proposed project builds on the groundwork laid down by the SNSF Sinergia project SMILE, which dealt with assessment of the manual activity of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) in isolated signs produced by early learners and L2 learners. SMILE-II will extend this technology to continuous sign language assessment including both manual and non-manual components of signs so that a DSGS learner’s sentence-level production can be assessed in an automatic manner.