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StyleBabel: Artistic Style Tagging and Captioning

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

We present StyleBabel, a unique open access dataset of natural language captions and free-form tags describing the artistic style of over 135K digital artworks, collected via a novel participatory method from experts studying at specialist art and design schools. StyleBabel was collected via an iterative method, inspired by ‘Grounded Theory’: a qualitative approach that enables annotation while co-evolving a shared language for fine-grained artistic style attribute description. We demonstrate several downstream tasks for StyleBabel, adapting the recent ALADIN architecture for fine-grained style similarity, to train cross-modal embeddings for: 1) free-form tag generation; 2) natural language description of artistic style; 3) fine-grained text search of style. To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and cross-modal representation learning, achieving a state of the art accuracy in fine-grained style retrieval.

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Notes

  1. 1.

    The dataset is released for open access (CC-BY 4.0).

  2. 2.

    https://miro.com/.

  3. 3.

    We redacted a minimal number of adult-themed images due to ethical considerations.

  4. 4.

    https://www.nltk.org/.

References

  1. von Ahn, L., Dabbish, L.A.: ESP: labeling images with a computer game, pp. 91–98 (2005)

    Google Scholar 

  2. Achlioptas, P., Ovsjanikov, M., Haydarov, K., Elhoseiny, M., Guibas, L.: Artemis: affective language for visual art. In: Proceedings of CVPR (2021)

    Google Scholar 

  3. Bai, Z., Nakashima, Y., Garcia, N.: Explain me the painting: multi-topic knowledgeable art description generation. CoRR, arXiv:2109.05743 (2021)

  4. Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. In: Proceedings of ACM SIGGRAPH (2015)

    Google Scholar 

  5. Bianco, S., Celona, L., Napoletano, P., Schettini, R.: Predicting image aesthetics with deep learning. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 117–125. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48680-2_11

    Chapter  Google Scholar 

  6. Kathy, C.: Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage, London (2006)

    Google Scholar 

  7. Cetinic, E., Grgic, S.: Automated painter recognition based on image feature extraction. In: Proceedings of ELMAR (2013)

    Google Scholar 

  8. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)

  9. Collomosse, J., Bui, T., Wilber, M., Fang, C., Jin, H.: Sketching with style: visual search with sketches and aesthetic context. In: Proceedings of ICCV (2017)

    Google Scholar 

  10. Cornia, M., Stefanini, M., Baraldi, L., Cucchiara, R.: Meshed-memory transformer for image captioning. arXiv preprint arXiv:1912.08226 (2020)

  11. Desai, K., Johnson, J.: Virtex: learning visual representations from textual annotations. CoRR, arXiv:2006.06666 (2020)

  12. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  13. Farhadi, A., et al.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 15–29. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_2

    Chapter  Google Scholar 

  14. Eric, F.: Art History and its Methods: A Critical Anthology. Phaidon, London (1995)

    Google Scholar 

  15. Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)

    Google Scholar 

  16. Garcia, N., Vogiatzis, G.: How to read paintings: semantic art understanding with multi-modal retrieval. CoRR, arXiv:1810.09617 (2018)

  17. Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)

  18. Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., Shlens, J.: Exploring the structure of a real-time, arbitrary neural artistic stylization network. arXiv preprint arXiv:1705.06830 (2017)

  19. Ghosal, K., Rana, A., Smolic, A.: Aesthetic image captioning from weakly-labelled photographs. CoRR, arXiv:1908.11310 (2019)

  20. Huang, L., Wang, W., Chen, J., Wei, X.: Attention on attention for image captioning. CoRR, arXiv:1908.06954 (2019)

  21. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of ICCV (2017)

    Google Scholar 

  22. Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_11

    Chapter  Google Scholar 

  23. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  24. Karayev, S., et al.: Recognizing image style. In: Proceedings of BMVC (2014)

    Google Scholar 

  25. Kulkarni, G., et al.: Babytalk: understanding and generating simple image descriptions. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2891–2903 (2013)

    Article  Google Scholar 

  26. Lavie, A., Agarwal, A.: Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments, pp. 228–231 (2007)

    Google Scholar 

  27. Xu, L., Meroño-Peñuela, A., Huang, Z., Harmelen, F.V.: An ontology model for narrative image annotation in the field of cultural heritage. In: WHiSe@ISWC (2017)

    Google Scholar 

  28. Li, X., et al.: Oscar: object-semantics aligned pre-training for vision-language tasks. arXiv preprint arXiv:2004.06165 (2020)

  29. Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.: Universal style transfer via feature transforms. In: Proceedings of NIPS (2017)

    Google Scholar 

  30. Lin, C.-Y.: Rouge: a package for automatic evaluation of summaries, p. 10 (2004)

    Google Scholar 

  31. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. CoRR, arXiv:1405.0312 (2014)

  32. Pang, K., Yang, Y., Hospedales, T.M., Xiang, T., Song, Y.: Solving mixed-modal jigsaw puzzle for fine-grained sketch-based image retrieval. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA, pp. 10344–10352. IEEE Computer Society (2020)

    Google Scholar 

  33. Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, USA, ACL 2002, pp. 311–318. Association for Computational Linguistics (2002)

    Google Scholar 

  34. Park, T., et al.: Swapping autoencoder for deep image manipulation. In: Proceedings of ECCV (2020)

    Google Scholar 

  35. Pinotti, A.: Formalism and the History of Style, pp. 75–90. Brill, Leiden (2012)

    Google Scholar 

  36. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)

  37. Ramesh, A., et al.: Zero-shot text-to-image generatio. arXiv preprint arXiv:2102.12092 (2021)

  38. Ramesh, A., et al.: Zero-shot text-to-image generation. arXiv preprint arXiv:2102.12092 (2021)

  39. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, arXiv:1506.01497 (2015)

  40. Ruder, M., Dosovitskiy, A., Brox, T.: Artistic style transfer for videos. In: Proceedings of GCPR (2016)

    Google Scholar 

  41. Ruta, D., et al.: Aladin: all layer adaptive instance normalization for fine-grained style similarity. arXiv preprint arXiv:2103.09776 (2021)

  42. Saleh, B., Elgammal, A.: Large-scale classification of fine-art paintings: learning the right metric on the right feature (2015)

    Google Scholar 

  43. Shamir, L., Macura, T., Orlov, N., Eckley, D.: Impressionism, expressionism, surrealism: automated recognition of painters and schools of art. IEEE Trans. Appl. Percept. (2010)

    Google Scholar 

  44. Simondsen, J., Roberton, T.: Routledge International Handbook of Participatory Design. Routledge, London (2013)

    Google Scholar 

  45. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  46. Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., Vaswani, A.: Bottleneck transformers for visual recognition. arXiv preprint arXiv:2101.11605 (2021)

  47. Ulyanov, D., Lebedev, V., Vedaldi, A., Lempitsky, V.: Texture networks: feed-forward synthesis of textures and stylized images. In: Proceedings of ICML (2016)

    Google Scholar 

  48. Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: consensus-based image description evaluation. CoRR, arXiv:1411.5726 (2014)

  49. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. arXiv preprint arXiv:1411.4555 (2015)

  50. Wang, X., Oxholm, G., Zhang, D., Wang, Y.-F.: Multimodal transfer: a hierarchical deep convolutional neural network for fast artistic style transfer. In: Proceedings of CVPR (2017)

    Google Scholar 

  51. Wei, X.-S., Luo, J.-H., Wu, J., Zhou, Z.-H.: Selective convolutional descriptor aggregation for fine-grained image retrieval. arXiv preprint arXiv:1604.04994 (2017)

  52. Wilber, M.J., Fang, C., Jin, H., Hertzmann, A., Collomosse, J., Belongie, S.: Bam! the behance artistic media dataset for recognition beyond photography. arXiv preprint arXiv:1704.08614 (2017)

  53. Xu, L., Wang, X.: Semantic description of cultural digital images: using a hierarchical model and controlled vocabulary. D Lib Mag. 21(5/6) (2015)

    Google Scholar 

  54. Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR 2011, pp. 1577–1584 (2011)

    Google Scholar 

  55. Zhang, P., et al.: Vinvl: revisiting visual representations in vision-language models. arXiv preprint arXiv:2101.00529 (2021)

  56. Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J.J., Gao, J.: Unified vision-language pre-training for image captioning and VQA. arXiv preprint arXiv:1909.11059 (2019)

  57. Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: an analysis of features and classifiers. In: Proceedings of IEEE Workshop on Multimedia Signal Processing (MMSP) (2009)

    Google Scholar 

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Acknowledgement

We would like to thank Thomas Gittings, Tu Bui, Alex Black, and Dipu Manandhar for their time, patience, and hard work, assisting with invigilating and managing the group annotation stages during data collection and annotation.

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Correspondence to Dan Ruta .

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Ruta, D. et al. (2022). StyleBabel: Artistic Style Tagging and Captioning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-20074-8_13

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