{"ID":2838631,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17282","arxiv_id":"2511.17282","title":"Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation","abstract":"Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts. Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity.","short_abstract":"Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural con...","url_abs":"https://arxiv.org/abs/2511.17282","url_pdf":"https://arxiv.org/pdf/2511.17282v1","authors":"[\"Chuancheng Shi\",\"Shangze Li\",\"Shiming Guo\",\"Simiao Xie\",\"Wenhua Wu\",\"Jingtong Dou\",\"Chao Wu\",\"Canran Xiao\",\"Cong Wang\",\"Zifeng Cheng\",\"Fei Shen\",\"Tat-Seng Chua\"]","published":"2025-11-21T14:40:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.CY\"]","methods":"[]","has_code":false}