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ViperGPT: Visual Inference via Python Execution for Reasoning
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About this Session
Answering queries about visual inputs is a complex task that requires both visual processing and reasoning. In this talk, I'll show you how large language models can be useful for reasoning in such settings that fall outside what you'd normally think of as language. ViperGPT uses a provided API to access computer vision modules, then composes them by generating Python code that is later executed. This simple approach requires no further training, and achieves state-of-the-art results across various complex visual tasks. I'll discuss how ViperGPT served as the inspiration for code-based agents, with thoughts on what the future of such agents may hold.
About the Speaker
Sachit Menon is a PhD student in Computer Science at Columbia University advised by Professor Carl Vondrick. His research centres around models trained at scale and ways to use them for novel tasks, such as using large language models to perform visual reasoning.
About Tech Talks
A regular series by Soroco, Tech Talks are expert-led technical sessions that deep dive into a specific area of technology and provide engineers valuable insights and tools. It also examines fascinating research, use cases and facilitates larger conversations around cutting-edge tech.
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