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Vision-language models (VLMs)

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This talk will chart the evolution of vision-language models (VLMs) and illustrate how architectural innovations and training paradigms have progressively closed the gap between visual perception and natural‐language understanding. I will cover models such as CLIP , Flamingo and LLaVA and discuss each of their design principles, strengths and weaknesses, and comparative performance across standard benchmarks.

This talk is part of the Machine Learning Reading Group @ CUED series.

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