DepictQA: Depicted Image Quality Assessment with Vision Language Models

DepictQA (Depicted image Quality Assessment) is dedicated to developing multi-modal image quality assessment models that linguistically assess or compare image quality, aiming to more closely align with human expressions.
News
[2024.07]    Datasets (huggingface / modelscope) of DepictQA-v1 and DepictQA-Wild (DepictQA-v2) were available.
[2024.07]    DepictQA-v1 was accepted to ECCV 2024.
[2024.06]    Codes of DepictQA-v1 and DepictQA-Wild (DepictQA-v2) were available.
[2024.05]    We released DepictQA-Wild (DepictQA-v2): a multi-functional in-the-wild descriptive image quality assessment model.
[2023.12]    We released DepictQA-v1, a multi-modal image quality assessment model based on vision language models.
Papers

*: Equal Contribution, †: Corresponding Author
arXiv, preprint
Descriptive Image Quality Assessment in the Wild
Zhiyuan You, Jinjin Gu, Zheyuan Li, Xin Cai, Kaiwen Zhu, Chao Dong†, Tianfan Xue†
arXiv, 2024
paper / project page / code / data

We introduce DepictQA-Wild, a multi-functional in-the-wild descriptive image quality assessment model.


Conference
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
Zhiyuan You*, Zheyuan Li*, Jinjin Gu*, Zhenfei Yin, Tianfan Xue†, Chao Dong†
ECCV, 2024
paper / project page / code / data

We introduce DepictQA, leveraging Multi-modal Large Language Models, allowing for detailed, language-based, and human-like evaluation of image quality.


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