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DynAI

Financiado

Omni-Supervised Learning for Dynamic Scene Understanding

Computer vision has become a powerful technology, able to bring applications such as autonomous vehicles and social robots closer to reality. In order for autonomous vehicles to safely navigate a scene, they need to understand the... ver más

31/12/2027

NVIDIA ITALY S.R.L...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

NVIDIA ITALY S.R.L. No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-11-28

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2021-STG: ERC STARTING GRANTS

I+D

Cerrada hace 3 años

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1 Participantes

NVIDIA ITALY S.R.L.

1.50M€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 61 meses Fecha Inicio: 2022-11-28
Fecha Fin: 2027-12-31

Líder del proyecto

NVIDIA ITALY S.R.L.

Presupuesto del proyecto 2M€

Descripción del proyecto Computer vision has become a powerful technology, able to bring applications such as autonomous vehicles and social robots closer to reality. In order for autonomous vehicles to safely navigate a scene, they need to understand the dynamic objects around it. In other words, we need computer vision algorithms to perform dynamic scene understanding (DSU), i.e., detection, segmentation, and tracking of multiple moving objects in a scene. This is an essential feature for higher-level tasks such as action recognition or decision making for autonomous vehicles. Much of the success of computer vision models for DSU has been driven by the rise of deep learning, in particular, convolutional neural networks trained on large-scale datasets in a supervised way. But the closed-world created by our datasets is not an accurate representation of the real world. If our methods only work on annotated object classes, what happens if a new object appears in front of an autonomous vehicle? We propose to rethink the deep learning models we use, the way we obtain data annotations, as well as the generalization of our models to previously unseen object classes. To bring all the power of computer vision algorithms for DSU to the open-world, we will focus on three lines of research: 1-Models. We will design novel machine learning models to address the shortcomings of convolutional neural networks. A hierarchical (from pixels to objects) image-dependent representation will allow us to capture spatio-temporal dependencies at all levels of the hierarchy. 2-Data. To train our models, we will create a new large-scale DSU synthetic dataset, and propose novel methods to mitigate the annotation costs for video data. 3-Open-World. To bring DSU to the open-world, we will design methods that learn directly from unlabeled video streams. Our models will be able to detect, segment, retrieve, and track dynamic objects coming from classes never previously observed during the training of our models.

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