ArtEmis: Affective Language for Art

  • Stanford University 1
  • LIX, Ecole Polytechnique 2
  • King Abdullah University of Science and Technology (KAUST) 3


  • [01.10.2021] The complete code & data will be released soon (ETA: 02-25-2021). Stay Tuned!!


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 439K emotion attributions and explanations from humans, on 81K 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.

Qualitative Results

Examples of neural speaker productions on unseen artworks. The produced explanations reflect a variety of dominant emotional-responses (shown above each utterance in bold font). The top row shows examples where the deduced grounding emotion is positive; the bottom row shows three examples where the grounding emotion is negative and an example from the something-else category. Remarkably, the neural speaker produces pragmatic explanations that include visual analogies: looks like blood, a dead animal, and nuanced explanations of affect: sad and lonely, expressive eyes.
Grounding the neural speaker with a desired emotion. These examples demonstrate the effect of grounding our neural speaker with a desired emotional reaction. The top row utterances are created by asking the neural speaker to react with contentment. For the examples of the bottom-row a variety of other dominant emotions are used (shown also in bold font). It is interesting how the speaker can adapt the explanation to reflect the desired/grounding emotion while staying relevant to the content of each painting.
Typical failure cases of neural generations. While the generations of our neural speakers are intriguing and a first step to a new direction: affective captioning; they have a long way to go before they become as soulful and diverse as their human-made counterparts. Here, we see how our neural speakers can make mistakes at the basic object-recognition level of reasoning e.g., (a) and (b), or mode-collapse to `vanilla'-like explanations e.g., (c) and (d).


Quick overview of ArtEmis.
Explore the ~80K artworks used in ArtEmis,
in a 8 minutes!


The ArtEmis dataset is released under the ArtEmis Terms of Use, and our code is released under the MIT license.



You can browse the ArtEmis annotations here.


If you find our work useful in your research, please consider citing:

                title={ArtEmis: Affective Language for Art},
                author={Achlioptas, Panos and Ovsjanikov, Maks and Haydarov,
                        Kilichbek and Elhoseiny, Mohamed and Guibas, Leonidas},
                journal = {CoRR},
                volume = {abs/2101.07396},


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This work is funded by a Vannevar Bush Faculty Fellowship, a KAUST BAS/1/1685-01-01, a CRG-2017-3426, the ERC Starting Grant No. 758800 (EXPROTEA) and the ANR AI Chair AIGRETTE, and gifts from the Adobe, Amazon AWS, Autodesk, and Snap corporations. The authors wish to thank Fei Xia and Jan Dombrowski for their help with the AMT instruction design and Nikos Gkanatsios for several fruitful discussions. The authors want to emphasize their gratitude to all the hard working Amazon Mechanical Turkers without whom this work would not be possible.