T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915198354325504 |
|---|---|
| author | Hosseini, Seyed Mohammad Hadi Izadi, Amir Mohammad Abdollahi, Ali Saghafian, Armin Baghshah, Mahdieh Soleymani |
| author_facet | Hosseini, Seyed Mohammad Hadi Izadi, Amir Mohammad Abdollahi, Ali Saghafian, Armin Baghshah, Mahdieh Soleymani |
| contents | Although recent text-to-image generative models have achieved impressive performance, they still often struggle with capturing the compositional complexities of prompts including attribute binding, and spatial relationships between different entities. This misalignment is not revealed by common evaluation metrics such as CLIPScore. Recent works have proposed evaluation metrics that utilize Visual Question Answering (VQA) by decomposing prompts into questions about the generated image for more robust compositional evaluation. Although these methods align better with human evaluations, they still fail to fully cover the compositionality within the image. To address this, we propose a novel metric that breaks down images into components, and texts into fine-grained questions about the generated image for evaluation. Our method outperforms previous state-of-the-art metrics, demonstrating its effectiveness in evaluating text-to-image generative models. Code is available at https://github.com/hadi-hosseini/ T2I-FineEval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation Hosseini, Seyed Mohammad Hadi Izadi, Amir Mohammad Abdollahi, Ali Saghafian, Armin Baghshah, Mahdieh Soleymani Computer Vision and Pattern Recognition Although recent text-to-image generative models have achieved impressive performance, they still often struggle with capturing the compositional complexities of prompts including attribute binding, and spatial relationships between different entities. This misalignment is not revealed by common evaluation metrics such as CLIPScore. Recent works have proposed evaluation metrics that utilize Visual Question Answering (VQA) by decomposing prompts into questions about the generated image for more robust compositional evaluation. Although these methods align better with human evaluations, they still fail to fully cover the compositionality within the image. To address this, we propose a novel metric that breaks down images into components, and texts into fine-grained questions about the generated image for evaluation. Our method outperforms previous state-of-the-art metrics, demonstrating its effectiveness in evaluating text-to-image generative models. Code is available at https://github.com/hadi-hosseini/ T2I-FineEval. |
| title | T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11481 |