AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909403519647744 |
|---|---|
| author | Xia, Jili He, Lihuo Gao, Fei Zhang, Kaifan Li, Leida Gao, Xinbo |
| author_facet | Xia, Jili He, Lihuo Gao, Fei Zhang, Kaifan Li, Leida Gao, Xinbo |
| contents | Recently, AI-generated images (AIGIs) created by given prompts (initial prompts) have garnered widespread attention. Nevertheless, due to technical nonproficiency, they often suffer from poor perception quality and Text-to-Image misalignment. Therefore, assessing the perception quality and alignment quality of AIGIs is crucial to improving the generative model's performance. Existing assessment methods overly rely on the initial prompts in the task prompt design and use the same prompts to guide both perceptual and alignment quality evaluation, overlooking the distinctions between the two tasks. To address this limitation, we propose a novel quality assessment method for AIGIs named TSP-MGS, which designs task-specific prompts and measures multi-granularity similarity between AIGIs and the prompts. Specifically, task-specific prompts are first constructed to describe perception and alignment quality degrees separately, and the initial prompt is introduced for detailed quality perception. Then, the coarse-grained similarity between AIGIs and task-specific prompts is calculated, which facilitates holistic quality awareness. In addition, to improve the understanding of AIGI details, the fine-grained similarity between the image and the initial prompt is measured. Finally, precise quality prediction is acquired by integrating the multi-granularity similarities. Experiments on the commonly used AGIQA-1K and AGIQA-3K benchmarks demonstrate the superiority of the proposed TSP-MGS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16087 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity Xia, Jili He, Lihuo Gao, Fei Zhang, Kaifan Li, Leida Gao, Xinbo Computer Vision and Pattern Recognition Recently, AI-generated images (AIGIs) created by given prompts (initial prompts) have garnered widespread attention. Nevertheless, due to technical nonproficiency, they often suffer from poor perception quality and Text-to-Image misalignment. Therefore, assessing the perception quality and alignment quality of AIGIs is crucial to improving the generative model's performance. Existing assessment methods overly rely on the initial prompts in the task prompt design and use the same prompts to guide both perceptual and alignment quality evaluation, overlooking the distinctions between the two tasks. To address this limitation, we propose a novel quality assessment method for AIGIs named TSP-MGS, which designs task-specific prompts and measures multi-granularity similarity between AIGIs and the prompts. Specifically, task-specific prompts are first constructed to describe perception and alignment quality degrees separately, and the initial prompt is introduced for detailed quality perception. Then, the coarse-grained similarity between AIGIs and task-specific prompts is calculated, which facilitates holistic quality awareness. In addition, to improve the understanding of AIGI details, the fine-grained similarity between the image and the initial prompt is measured. Finally, precise quality prediction is acquired by integrating the multi-granularity similarities. Experiments on the commonly used AGIQA-1K and AGIQA-3K benchmarks demonstrate the superiority of the proposed TSP-MGS. |
| title | AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16087 |