Papers with feature enhancement

1 papers
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)

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Challenge: Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach: They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome: The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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