DESCRIPTION :
category-specific representations and then to entangle pertinent cues of each expert by exploiting the semantic intercorrelation between them. Further, existing anomaly detection methods primarily focus on immediate detection, lacking the capability to anticipate anomalies well in advance. This shortcoming is particularly critical in systems where early warning can prevent anomalies. By leveraging the strengths of auto-regressive models, which predict future values based on historical data, we aim to extend the predictive horizon, allowing for timely and informed decision-making., The candidate is expected to conduct research related to the development of computer vision algorithms for video understanding.
Main activities:
* Analyze the requirements of end-users and study the limitations of existing solutions.
* Propose a new algorithm for detecting video anomalies (wVAD)
* Evaluate and optimize the proposed algorithm on the targeted video datasets
* Oral presentation and writing reports
* Submit a scientific paper to a conference
Code d'emploi : Thésard (h/f)
Niveau de formation : Bac+5
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Vision par Ordinateur, C ++ (Langage de Programmation), Programmation Informatique, Linux, Python (Langage de Programmation), Machine Learning, OpenCV, Tensorflow, Pytorch, Deep Learning, Technologies Informatiques, Anglais, Prise de Décision, Leadership, Prise de Parole en Publique, Enthousiasme, Stabilité Émmotionnelle, Esprit d'Équipe, Implication et Investissement, Innovation, Recherche, Algorithmes, Organisation d'Événements, Mathématiques, Analyses Prédictives, Recherche Scientifique, Rédaction de Rapports, Détection D'anomalies
Courriel :
Francois.Bremond@inria.fr
Téléphone :
0493653043
Type d'annonceur : Employeur direct