EditCaption: Human-Aligned Instruction Synthesis for Image Editing via Supervised Fine-Tuning and Direct Preference Optimization

EditCaption introduces a two-stage pipeline to enhance vision-language models for generating accurate instructions in image editing tasks, addressing common errors like orientation inconsistency and viewpoint ambiguity. This approach significantly improves the quality of synthesized instructions, reducing critical errors by more than half and increasing correctness from 41.75% to 66%, making it valuable for developers working on instruction-guided image editing systems.

Read the full article at arXiv cs.CV (Vision)

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