Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.07265 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909912659918848 |
|---|---|
| author | Niu, Yuwei Ning, Munan Zheng, Mengren Jin, Weiyang Lin, Bin Jin, Peng Liao, Jiaqi Feng, Chaoran Ning, Kunpeng Zhu, Bin Yuan, Li |
| author_facet | Niu, Yuwei Ning, Munan Zheng, Mengren Jin, Weiyang Lin, Bin Jin, Peng Liao, Jiaqi Feng, Chaoran Ning, Kunpeng Zhu, Bin Yuan, Li |
| contents | Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image alignment, lacking a comprehensive assessment of complex semantic understanding and world knowledge integration in text-to-image generation. To address this challenge, we propose \textbf{WISE}, the first benchmark specifically designed for \textbf{W}orld Knowledge-\textbf{I}nformed \textbf{S}emantic \textbf{E}valuation. WISE moves beyond simple word-pixel mapping by challenging models with 1000 meticulously crafted prompts across 25 subdomains in cultural common sense, spatio-temporal reasoning, and natural science. To overcome the limitations of traditional CLIP metric, we introduce \textbf{WiScore}, a novel quantitative metric for assessing knowledge-image alignment. Through comprehensive testing of 20 models (10 dedicated T2I models and 10 unified multimodal models) using 1,000 structured prompts spanning 25 subdomains, our findings reveal significant limitations in their ability to effectively integrate and apply world knowledge during image generation, highlighting critical pathways for enhancing knowledge incorporation and application in next-generation T2I models. Code and data are available at \href{https://github.com/PKU-YuanGroup/WISE}{PKU-YuanGroup/WISE}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation Niu, Yuwei Ning, Munan Zheng, Mengren Jin, Weiyang Lin, Bin Jin, Peng Liao, Jiaqi Feng, Chaoran Ning, Kunpeng Zhu, Bin Yuan, Li Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language I.2.7; I.2.10; I.4.9 Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image alignment, lacking a comprehensive assessment of complex semantic understanding and world knowledge integration in text-to-image generation. To address this challenge, we propose \textbf{WISE}, the first benchmark specifically designed for \textbf{W}orld Knowledge-\textbf{I}nformed \textbf{S}emantic \textbf{E}valuation. WISE moves beyond simple word-pixel mapping by challenging models with 1000 meticulously crafted prompts across 25 subdomains in cultural common sense, spatio-temporal reasoning, and natural science. To overcome the limitations of traditional CLIP metric, we introduce \textbf{WiScore}, a novel quantitative metric for assessing knowledge-image alignment. Through comprehensive testing of 20 models (10 dedicated T2I models and 10 unified multimodal models) using 1,000 structured prompts spanning 25 subdomains, our findings reveal significant limitations in their ability to effectively integrate and apply world knowledge during image generation, highlighting critical pathways for enhancing knowledge incorporation and application in next-generation T2I models. Code and data are available at \href{https://github.com/PKU-YuanGroup/WISE}{PKU-YuanGroup/WISE}. |
| title | WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language I.2.7; I.2.10; I.4.9 |
| url | https://arxiv.org/abs/2503.07265 |