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.
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[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.
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Papers
*: Equal Contribution, †: Corresponding Author
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arXiv, preprint
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Descriptive Image Quality Assessment in the Wild
Zhiyuan You,
Jinjin Gu, Zheyuan Li, Xin Cai, Kaiwen Zhu, Chao Dong†, Tianfan Xue†
arXiv, 2024
paper
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project page
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code
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data
We introduce DepictQA-Wild, a multi-functional in-the-wild descriptive image quality
assessment model.
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Conference
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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
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project page
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code
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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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