TIFA

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A paper that proposes using few-shot learning to decompose a prompt into atomic properties (yes/no questions) for quantitative evaluation of text-to-image faithfulness by MLLMs.

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Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 7 - Evaluation

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A paper that proposes using few-shot learning to decompose a prompt into atomic properties (yes/no questions) for quantitative evaluation of text-to-image faithfulness by MLLMs.