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PRINNEVOT

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Towards Primate-like Artificial Neural Networks for Visual Object Tracking

The PRINNEVOT project embarks on a mission to bridge the gap between computer vision and the primate visual system in the context of Visual Object Tracking (VOT). VOT is the task of maintaining focus The PRINNEVOT project embarks... see more

31/10/2027

UNIUD

256K€

Project Budget: 256K€

Project leader

UNIVERSITA DEGLI STUDI DI UDINE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-04-18

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 Expected Outcome:Project results are expected to contribute to the following outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

UNIUD

256.44K€ | Leader

YORK UNIVERSITY

YORKU

N.D. | participant

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Project duration: 42 months Date Start: 2024-04-18
End date: 2027-10-31

Project leader

UNIVERSITA DEGLI STUDI DI UDINE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 256K€

participation deadline Sin fecha límite de participación.

Project description The PRINNEVOT project embarks on a mission to bridge the gap between computer vision and the primate visual system in the context of Visual Object Tracking (VOT). VOT is the task of maintaining focus The PRINNEVOT project embarks on a mission to bridge the gap between computer vision and the primate visual system in the context of Visual Object Tracking (VOT). VOT is the task of maintaining focus on a specific object amidst a dynamic visual environment. Our brains excel at it but replicating this ability in artificial vision systems remains a challenge. This project seeks to develop a novel class of VOT algorithms inspired by the primate visual system's prowess. Despite notable advancements in deep learning-based VOT over the past decade, these algorithms still fall short in emulating the robustness exhibited by primate vision. PRINNEVOT will address this gap through a multi-faceted approach. Firstly, PRINNEVOT will construct a comprehensive reference dataset, investigating both primate behavior and neural recordings. Secondly, among the existing artificial neural network (ANN)-based VOT methodologies, the project aims to identify those that align most closely with the primate brain's mechanisms. Lastly, PRINNEVOT will leverage the discovered inductive biases to develop a new ANN architecture for VOT that closely mirrors the primate's way of continuous object recognition and localization. By merging computer vision and computational neuroscience research, PRINNEVOT aspires to contribute to the development of more accurate and robust VOT algorithms. These algorithms, in alignment with the European Union's pursuit of safer and ethically grounded Artificial Intelligence, promise to enhance human-centric and trustworthy technologies. Furthermore, the project's outcomes will not only benefit AI and computer vision but also advance our understanding of the primate visual system, offering new empirical models of how the brain tracks objects in dynamic visual environments.

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