Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.10019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909646233534464 |
|---|---|
| author | Lan, Tian Zhou, Yang-Hao Ma, Zi-Ao Sun, Fanshu Sun, Rui-Qing Luo, Junyu Tu, Rong-Cheng Huang, Heyan Xu, Chen Wu, Zhijing Mao, Xian-Ling |
| author_facet | Lan, Tian Zhou, Yang-Hao Ma, Zi-Ao Sun, Fanshu Sun, Rui-Qing Luo, Junyu Tu, Rong-Cheng Huang, Heyan Xu, Chen Wu, Zhijing Mao, Xian-Ling |
| contents | Recent advances in deep learning have significantly enhanced generative AI capabilities across text, images, and audio. However, automatically evaluating the quality of these generated outputs presents ongoing challenges. Although numerous automatic evaluation methods exist, current research lacks a systematic framework that comprehensively organizes these methods across text, visual, and audio modalities. To address this issue, we present a comprehensive review and a unified taxonomy of automatic evaluation methods for generated content across all three modalities; We identify five fundamental paradigms that characterize existing evaluation approaches across these domains. Our analysis begins by examining evaluation methods for text generation, where techniques are most mature. We then extend this framework to image and audio generation, demonstrating its broad applicability. Finally, we discuss promising directions for future research in cross-modal evaluation methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations Lan, Tian Zhou, Yang-Hao Ma, Zi-Ao Sun, Fanshu Sun, Rui-Qing Luo, Junyu Tu, Rong-Cheng Huang, Heyan Xu, Chen Wu, Zhijing Mao, Xian-Ling Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Recent advances in deep learning have significantly enhanced generative AI capabilities across text, images, and audio. However, automatically evaluating the quality of these generated outputs presents ongoing challenges. Although numerous automatic evaluation methods exist, current research lacks a systematic framework that comprehensively organizes these methods across text, visual, and audio modalities. To address this issue, we present a comprehensive review and a unified taxonomy of automatic evaluation methods for generated content across all three modalities; We identify five fundamental paradigms that characterize existing evaluation approaches across these domains. Our analysis begins by examining evaluation methods for text generation, where techniques are most mature. We then extend this framework to image and audio generation, demonstrating its broad applicability. Finally, we discuss promising directions for future research in cross-modal evaluation methodologies. |
| title | A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.10019 |