research-article

AesCLIP: Multi-Attribute Contrastive Learning for Image Aesthetics Assessment

Authors:

Xiangfei Sheng,

Guangming ShiAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 1117 - 1126

https://doi.org/10.1145/3581783.3611969

Published: 27 October 2023 Publication History

Abstract

Image aesthetics assessment (IAA) aims at predicting the aesthetic quality of images. Recently, large pre-trained vision-language models, like CLIP, have shown impressive performances on various visual tasks. When it comes to IAA, a straightforward way is to finetune the CLIP image encoder using aesthetic images. However, this can only achieve limited success without considering the uniqueness of multimodal data in the aesthetics domain. People usually assess image aesthetics according to fine-grained visual attributes, e.g., color, light and composition. However, how to learn aesthetics-aware attributes from CLIP-based semantic space has not been addressed before. With this motivation, this paper presents a CLIP-based multi-attribute contrastive learning framework for IAA, dubbed AesCLIP. Specifically, AesCLIP consists of two major components, i.e., aesthetic attribute-based comment classification and attribute-aware learning. The former classifies the aesthetic comments into different attribute categories. Then the latter learns an aesthetic attribute-aware representation by contrastive learning, aiming to mitigate the domain shift from the general visual domain to the aesthetics domain. Extensive experiments have been done by using the pre-trained AesCLIP on four popular IAA databases, and the results demonstrate the advantage of AesCLIP over the state-of-the-arts. The source code will be public at https://github.com/OPPOMKLab/AesCLIP.

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Cited By

Huang YLi LChen PWu HLin WShi G(2025)Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics ComputingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.349225947:2(1205-1218)Online publication date: Feb-2025
https://doi.org/10.1109/TPAMI.2024.3492259
He SXiao YMing AMa H(2025)Prompt-guided image color aesthetics assessment: Models, datasets and benchmarksInformation Fusion10.1016/j.inffus.2024.102706114(102706)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102706
Zhu HShi JShao ZYao RZhou YZhao JLi LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Attribute-Driven Multimodal Hierarchical Prompts for Image Aesthetic Quality AssessmentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681175(2399-2408)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681175
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Index Terms

AesCLIP: Multi-Attribute Contrastive Learning for Image Aesthetics Assessment
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
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Tao Mei
HiDream.ai, China
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Rita Cucchiara
University of Modena and Reggio Emilia, Italy
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Program Chairs:
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University of Florence, Italy
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Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
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Pradeep K. Atrey
University at Albany, State University of New York, USA
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M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

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Cited By

Huang YLi LChen PWu HLin WShi G(2025)Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics ComputingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.349225947:2(1205-1218)Online publication date: Feb-2025
https://doi.org/10.1109/TPAMI.2024.3492259
He SXiao YMing AMa H(2025)Prompt-guided image color aesthetics assessment: Models, datasets and benchmarksInformation Fusion10.1016/j.inffus.2024.102706114(102706)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102706
Zhu HShi JShao ZYao RZhou YZhao JLi LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Attribute-Driven Multimodal Hierarchical Prompts for Image Aesthetic Quality AssessmentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681175(2399-2408)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681175
Xie RMing AHe SXiao YMa HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)"Special Relativity" of Image Aesthetics Assessment: a Preliminary Empirical PerspectiveProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681172(2554-2563)Online publication date: 28-Oct-2024
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Gao FLin YShi JQiao MWang NCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)AesMamba: Universal Image Aesthetic Assessment with State Space ModelsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681011(7444-7453)Online publication date: 28-Oct-2024
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Yang ZLi LChen PWu JDong WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Semantics-Aware Image Aesthetics Assessment using Tag Matching and Contrastive RankingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680972(2632-2641)Online publication date: 28-Oct-2024
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Huang YSheng XYang ZYuan QDuan ZChen PLi LLin WShi GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics PerceptionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680649(5911-5920)Online publication date: 28-Oct-2024
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Huang YLi LChen PWu JYang YLi YShi G(2024)Coarse-to-Fine Image Aesthetics Assessment With Dynamic Attribute SelectionIEEE Transactions on Multimedia10.1109/TMM.2024.338945226(9316-9329)Online publication date: 16-Apr-2024
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Kastryulin SProkopenko DBabenko ADylov D(2024)QUASAR: QUality and Aesthetics Scoring With Advanced RepresentationsIEEE Access10.1109/ACCESS.2024.348701012(160946-160956)Online publication date: 2024
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Yan SXu SLei AZhang S(2024)Advancing neural aesthetic assessment of artistic images based on bundle features integrationThe Visual Computer10.1007/s00371-024-03732-5Online publication date: 10-Dec-2024
https://doi.org/10.1007/s00371-024-03732-5

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