Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI. Existing T2I evaluation metrics and multimodal judges often rely on visual-semantic representations that underrepresent implicit cultural norms, leading to biased preference judgments and the omission of fine-grained cultural cues. In addition, visual question answering (VQA)-based evaluators typically depend on autoregressive text generation, which limits their scalability for real-time reward modeling. To address these limitations, we introduce an Implicit Cultural Alignment Reward Model built upon a lightwe...
| Authors | Bo-An Chang, Yu-Chih Chen |
|---|---|
| Primary category | cs.CV |
| Published | 17 July 2026 |
| Last updated at source | 16 September 2026 |
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