AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework

TL;DR

AdaptaGen introduces a hierarchical semantic optimization framework for domain-specific image generation, effectively reducing hallucinations and improving semantic accuracy and diversity by integrating prompt optimization, multi-perspective understanding, and cross-modal adaptation.

Contribution

The paper presents a novel hierarchical semantic optimization framework that jointly addresses prompt engineering and model adaptation for improved domain-specific image synthesis.

Findings

Achieves superior image quality and diversity across 40 categories.

Effectively reduces semantic hallucinations and deviations.

Demonstrates significant improvements with limited data (16 images per category).

Abstract

Domain-specific image generation aims to produce high-quality visual content for specialized fields while ensuring semantic accuracy and detail fidelity. However, existing methods exhibit two critical limitations: First, current approaches address prompt engineering and model adaptation separately, overlooking the inherent dependence between semantic understanding and visual representation in specialized domains. Second, these techniques inadequately incorporate domain-specific semantic constraints during content synthesis, resulting in generation outcomes that exhibit hallucinations and semantic deviations. To tackle these issues, we propose AdaptaGen, a hierarchical semantic optimization framework that integrates matrix-based prompt optimization with multi-perspective understanding, capturing comprehensive semantic relationships from both global and local perspectives. To mitigate hallucinations in specialized domains, we design a cross-modal adaptation mechanism, which, when combined with intelligent content synthesis, enables preserving core thematic elements while incorporating diverse details across images. Additionally, we introduce a two-phase caption semantic transformation during the generation phase. This approach maintains semantic coherence while enhancing visual diversity, ensuring the generated images adhere to domain-specific constraints. Experimental results confirm our approach's effectiveness, with our framework achieving superior performance across 40 categories from diverse datasets using only 16 images per category, demonstrating significant improvements in image quality, diversity, and semantic consistency.