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Towards Primate-like Artificial Neural Networks for Visual Object Tracking

Project description

A closer look at artificial vision systems

Maintaining focus on an object amidst a changing visual scene is effortless for our brains, but remains a significant challenge for artificial vision systems. Despite advancements, existing algorithms fall short of replicating the robustness of the primate visual system. Supported by the Marie Skłodowska-Curie Actions (MSCA) programme, the PRINNEVOT project bridges the gap between computer vision and primate visual processing. By constructing a reference dataset, identifying neural network methodologies aligning with primate brain mechanisms, PRINNEVOT seeks to advance both AI and our understanding of the primate visual system. This objective is in line with the EU’s pursuit of ethical AI, promising safer and more trustworthy technologies.

Objective

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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HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

UNIVERSITA DEGLI STUDI DI UDINE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 256 442,88
Address
VIA PALLADIO 8
33100 Udine
Italy

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Region
Nord-Est Friuli-Venezia Giulia Udine
Activity type
Higher or Secondary Education Establishments
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Total cost

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