ArtEmis: Affective Language for Visual Art
Open accessAbstract
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice. As we demonstrate below, this leads to a rich set of signals for both the objective content and the affective impact of an image, creating associations with abstract concepts (e.g., "freedom" or "love"), or references that go beyond what is directly visible, including visual similes and metaphors, or subjective references to personal experiences. We focus on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. Our dataset, termed ArtEmis, contains 455K emotion attributions and explanations from humans, on 80K artworks from WikiArt. Building on this data, we train and demonstrate a series of captioning systems capable of expressing and explaining emotions from visual stimuli. Remarkably, the captions produced by these systems often succeed in reflecting the semantic and abstract content of the image, going well beyond systems trained on existing datasets. The collected dataset and developed methods are available at https://artemisdataset.org.
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Transformer based cross-modal alignment has been studied for various tasks, e.g., speaker localization, speech translation, text-to-speech alignment, text-to-video retrieval, and visual grounding of natural language.
“Transformer based cross-modal alignment has been studied for various tasks, e.g., speaker localization in multi-speaker videos [250], speech translation [180], text-to-speech alignment [251], text-to-video retrieval [252], [253], [254], and visual grounding of natural language [255], [256], [257], [258], [259].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [250]
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Data-efficient image captioning of fine art paintings via virtual-real semantic alignment training
Lu, Y., Guo, C., Dai, X., & Wang, F. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.068 · Cited by 42 · see citing works
Reimagining personalization in the physical store
Scholdra, T. P., Wichmann, J. R. K., & Reinartz, W. (2023) · Journal of Retailing · doi:10.1016/j.jretai.2023.11.001 · Cited by 45 · see citing works
ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity
Ruta, D., Motiian, S., Faieta, B., Lin, Z., Jin, H., Filipkowski, A., Gilbert, A., & Collomosse, J. P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01171 · Cited by 39 · see citing works
AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
Huang, Y., Sheng, X., Yang, Z., Yuan, Q., Duan, Z., Chen, P., Li, L., Lin, W., & Shi, G. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680649 · Cited by 47 · see citing works
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Self-citationGuo, Y., Siddiqui, F., Zhao, Y., Chellappa, R., & Lo, S.-Y. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02495-3 · Cited by 32 · see citing works
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Wong, M., Ong, Y.-S., Gupta, A., Bali, K. K., & Chen, C. (2023) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai54212.2023.00105 · Cited by 29 · see citing works
MetaCLUE: Towards Comprehensive Visual Metaphors Research
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Self-citationLima, R., Chirico, A., Varandas, R. S. P., Gamboa, H. F. S., Gaggioli, A., & Badia, S. B. I. (2024) · Virtual Reality · doi:10.1007/s10055-024-00989-y · Cited by 27 · see citing works
MASANet: Multi-Aspect Semantic Auxiliary Network for Visual Sentiment Analysis
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Using machine learning to predict artistic styles: an analysis of trends and the research agenda
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Reducing Bias in AI-based Analysis of Visual Artworks
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Affection: Learning Affective Explanations for Real-World Visual Data
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Self-citationFeature fusion via multi-target learning for ancient artwork captioning
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AffectGAN: Affect-Based Generative Art Driven by Semantics
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Self-citationAffective Image Filter: Reflecting Emotions from Text to Images
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Multifaceted and deep semantic alignment network for multimodal sarcasm detection
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TRTST: Arbitrary High-Quality Text-Guided Style Transfer With Transformers
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S$$^{2}$$-VER: Semi-supervised Visual Emotion Recognition
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Image Captioning on Fine Art Paintings via Virtual Paintings
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ArtCap: A Dataset for Image Captioning of Fine Art Paintings
Lu, Y., Guo, C., Dai, X., & Wang, F. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3223539 · Cited by 13 · see citing works
FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations
Suresh, A. P., Jain, S., Noinongyao, P., Ganguly, A., Watchareeruetai, U., & Samacoïts, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00715 · Cited by 13 · see citing works
Mind captioning: Evolving descriptive text of mental content from human brain activity
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StyleBabel: Artistic Style Tagging and Captioning
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Evaluation in Neural Style Transfer: A Review
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Self-citationTraining A Small Emotional Vision Language Model for Visual Art Comprehension
Zhang, J., Zheng, L., Wang, M., & Guo, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_23 · Cited by 11 · see citing works
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Affective Image Captioning for Visual Artworks Using Emotion-Based Cross-Attention Mechanisms
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Probing Sentiment-Oriented PreTraining Inspired by Human Sentiment Perception Mechanism
Feng, T., Liu, J., & Yang, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00279 · Cited by 11 · see citing works
OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?
Li, R., Sun, S., Elhoseiny, M., & Torr, P. H. S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01856 · Cited by 10 · see citing works
Self-citationA novel flexible identity-net with diffusion models for painting-style generation
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Art Creation with Multi-Conditional StyleGANs
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González-Martín, C., Carrasco, M., & Wielandt, T. G. W. (2023) · Empirical Studies of the Arts · doi:10.1177/02762374231163481 · Cited by 8 · see citing works
WDANet: Exploring Stylized Animation via Diffusion Model for Woodcut‐Style Design
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EMOTION MODELING IN SCULPTURE DESIGN USING NEURAL NETWORKS
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Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings
Aslan, S., Castellano, G., Digeno, V., Migailo, G., Scaringi, R., & Vessio, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13321-3_12 · Cited by 8 · see citing works
Hypericons for interpretability: decoding abstract concepts in visual data
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Adversarial Text to Continuous Image Generation
Haydarov, K., Muhamed, A., Shen, X., Lazarević, J., Skorokhodov, I., Galappaththige, C. J., & Elhoseiny, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00604 · Cited by 7 · see citing works
Self-citationGenerating Emotion Descriptions for Fine Art Paintings Via Multiple Painting Representations
Lu, Y., Guo, C., Dai, X., & Wang, F. (2023) · IEEE Intelligent Systems · doi:10.1109/mis.2023.3260992 · Cited by 8 · see citing works
Language-Driven Image Style TransferPreprint
Fu, T.-J., Wang, X. E., & Wang, W. Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00178 · Cited by 7 · see citing works
Hu, S., Wang, H., Zhang, Y., Wang, P., & Lu, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710954 · Cited by 10 · see citing works
LAPIS: A Novel Dataset for Personalized Image Aesthetic Assessment
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Impressions: Visual Semiotics and Aesthetic Impact Understanding
Kruk, J., Ziems, C., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.755 · Cited by 7 · see citing works
ImageCaptioner2: Image Captioner for Image Captioning Bias Amplification Assessment
Abdelrahman, E., Sun, P., Li, L. E., & Elhoseiny, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30080 · Cited by 6 · see citing works
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Shao, L., Agaian, S. С., & Grigoryan, A. M. (2024) · Applied Sciences · doi:10.3390/app142311235 · Cited by 6 · see citing works
Affective word embedding in affective explanation generation for fine art paintings
Yan, J., Wang, W., & Yu, C. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.07.009 · Cited by 6 · see citing works
FeelingBlue: A Corpus for Understanding the Emotional Connotation of Color in Context
Ananthram, A., Winn, O., & Muresan, S. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00540 · Cited by 6 · see citing works
Exploring Emotional Stimuli Detection in Artworks: A Benchmark Dataset and Baselines Evaluation
Chen, T., García, N., Li, L., & Nakashima, Y. (2024) · Journal of Imaging · doi:10.3390/jimaging10060136 · Cited by 5 · see citing works
Bridging the Gap: Decoding Abstract Concepts in Cultural Heritage Images
Pandiani, D. S. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-57675-1_8 · Cited by 5 · see citing works
Cultural-Aware AI Model for Emotion Recognition
Baradaran, M., Zohari, P., Mahyar, A., Motamednia, H., Rahmati, D., & Gorgin, S. (2024) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip62238.2024.10491176 · Cited by 5 · see citing works
Socially Aware Language Technologies: Perspectives and Practices
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Castellano, G., Scaringi, R., & Vessio, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47546-7_29 · Cited by 5 · see citing works
Generation of Images from Text Using AI
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Self-citationQuantitative Analysis of Visual Representation of Sign Elements in COVID-19 Context
Cano-Martínez, M. J., Carrasco, M., Sandoval, J., & González-Martín, C. (2022) · Empirical Studies of the Arts · doi:10.1177/02762374221104059 · Cited by 5 · see citing works
D-ViSA: A Dataset for Detecting Visual Sentiment from Art Images
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Understanding of Emotion Perception from ArtPreprint
Bose, D., Somandepalli, K., Kundu, S., Lahiri, R., Gratch, J. S., & Narayanan, S. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06486 · Cited by 7 · see citing works
EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation
Zhang, C., Xie, H., Wen, B., Zuo, S., Zhang, R., & Cheng, W.-H. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758201 · Cited by 4 · see citing works
Knowledge Enhanced and Incongruity Perceiving Network for Multimodal Sarcasm Detection
Liu, M., & Li, Z. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10499-x · Cited by 4 · see citing works
Lu, T., & Hu, J. (2023) · International Association of Societies of Design Research Congress (IASDR) · doi:10.21606/iasdr.2023.101 · Cited by 4 · see citing works
Cross-Linguistic Study on Affective Impression and Language for Visual Art Using Neural Speaker
Narimatsu, H., Ueda, R., & Kumano, S. (2022) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii55700.2022.9953880 · Cited by 4 · see citing works
Affective Image Editing: Shaping Emotional Factors via Text Descriptions
Zhang, P., Weng, S., Zhu, C., Tang, B., Jia, Z., Li, S., & Shi, B. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02678-y · Cited by 3 · see citing works
Understanding Museum Exhibits using Vision-Language Reasoning
Balauca, A.-A., Garai, S., Balauca, S., Shetty, R. U., Agrawal, N., Shah, D., Fu, Y., Wang, X., Toutanova, K., Paudel, D. P., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00215 · Cited by 3 · see citing works
Finding Beautiful and Happy Images for Mental Health and Well-Being Applications
Xie, R., Qiu, C. S., & Qiu, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18913-5_54 · Cited by 3 · see citing works
Confidence-aware calibration and scoring functions for curriculum learning
Ao, S., Rueger, S., & Siddharthan, A. (2023) · International Conference on Machine Vision · doi:10.1117/12.2679353 · Cited by 3 · see citing works
Computational tracking of head pose through 500 years of fine-art portraiture
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Cogeneration of Innovative Audio-visual Content: A New Challenge for Computing Art
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From Canvas to Code: the Evolution of Generative Art in the AI Era
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Robot Synesthesia: A Sound and Emotion Guided Robot Painter
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Fine-grained aesthetic multi-attribute captioning with aligned vision-language representations
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Stimuli-Aware Emotion Adaptor for Enhancing LLM in Affective Explanation Captioning
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A Unified Model for Style Classification and Emotional Response Analysis
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Practice architectures for bridging the semantic gap in museum documentation
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Turning Paintings into Multimodal Digital Objects
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Bright as the Sun: In-depth Analysis of Imagination-Driven Image Captioning
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DecoupleCLIP: A Novel Cross-Modality Decouple Model for Painting Captioning
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Zhang, F., Zhang, J., Wang, C., & Li, Y. (2025) · IET Image Processing · doi:10.1049/ipr2.70235 · Cited by 1 · see citing works
Das, S. K., & Bissoyi, S. (2026) · AI and Ethics · doi:10.1007/s43681-026-01044-z · Cited by 1 · see citing works
ViPE: Visualise Pretty-much Everything
Shahmohammadi, H., Ghosh, A., & Lensch, H. P. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.333 · Cited by 1 · see citing works
- Authors
- Achlioptas, P., Ovsjanikov, M., Haydarov, K., Elhoseiny, M., & Guibas, L.
- Affiliations
- Stanford University (US)
- École Polytechnique (FR)
- King Abdullah University of Science and Technology (SA)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 11564-11574
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3124944919
- Cited by
- 161
- References
- 70
- Citations by year
- 2026: 29 · 2025: 31 · 2024: 35 · 2023: 39 · 2022: 20 · 2021: 7
- FWCI
- 8.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (2017-)
- King Abdullah University of Science and Technology (BAS/1/1685-01-01, CRG-2017-3426)
- H2020 European Research Council (758800)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- affective language, visual arts, emotion annotation, WikiArt, audience emotional response
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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