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What can Large Language Models offer to Event-based Vision?
This project focuses on combining Large Language Models within the area of Event-based Computer Vision.
Keywords: Event-based Vision, Computer Vision, Large Language Models, GPT, Natural Language Processing
Event-based vision algorithms process visual changes in an asynchronous manner akin to how biological visual systems function, while large language models (LLMs) specialize in parsing and generating human-like text. This project aims to explore the intersection of Large Language Models (LLMs) and Event-based Vision, leveraging the unique capabilities of each domain to create a symbiotic framework. By marrying the strengths of both technologies, the initiative aims to develop a novel, more robust paradigm that excels in challenging conditions.
Event-based vision algorithms process visual changes in an asynchronous manner akin to how biological visual systems function, while large language models (LLMs) specialize in parsing and generating human-like text. This project aims to explore the intersection of Large Language Models (LLMs) and Event-based Vision, leveraging the unique capabilities of each domain to create a symbiotic framework. By marrying the strengths of both technologies, the initiative aims to develop a novel, more robust paradigm that excels in challenging conditions.
The primary objective is to devise methodologies that synergize the capabilities of LLMs with Event-Based Vision systems. We intend to address identified shortcomings in existing paradigms by leveraging the inferential strengths of LLMs. Rigorous evaluations will be conducted to validate the efficacy of the integrated system under various challenging conditions.
The primary objective is to devise methodologies that synergize the capabilities of LLMs with Event-Based Vision systems. We intend to address identified shortcomings in existing paradigms by leveraging the inferential strengths of LLMs. Rigorous evaluations will be conducted to validate the efficacy of the integrated system under various challenging conditions.
Nikola Zubic (zubic@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch)
Nikola Zubic (zubic@ifi.uzh.ch), Nico Messikommer (nmessi@ifi.uzh.ch)