Papers by Cyrus Rashtchian

1 papers
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback (2025.naacl-long)

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Challenge: Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user’s input text.
Approach: They propose a training algorithm that trains T2I models to be faithful to the input text.
Outcome: The proposed model improves both the semantic alignment and aesthetic appeal of two diffusion-based T2I models, evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA aesthetic).

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