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AI in fashion: a literature review

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Abstract

Artificial Intelligence (AI) has a growing influence in the fashion industry. In this review study, the focal points of research in AI in the context of fashion are showcased. This is achieved by quantifying the amount of research conducted in this area. Various insights, that could be useful for future studies are also provided. For each included study, the particular objective, that AI is tasked to achieve, is identified, as well as the methods and evaluation metrics that have been utilized in order to do so. A potential question which is also answered through this study, is which fashion items are targeted by researchers. Lastly, information about the utilized datasets is provided.

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References

  1. Giri, C., Jain, S., Zeng, X., & Bruniaux, P. (2019). A detailed review of artificial intelligence applied in the fashion and apparel industry. IEEE Access, 7, 95376��95396.

    Article  Google Scholar 

  2. Mohammadi, S.O., & Kalhor, A. (2021). Smart fashion: A review of AI applications in the fashion & apparel industry. arXiv preprint arXiv:2111.00905

  3. Pillarisetty, R., & Mishra, P. (2022). A review of AI (artificial intelligence) tools and customer experience in online fashion retail. International Journal of E-Business Research (IJEBR), 18(2), 1–12.

    Google Scholar 

  4. Lomas, L.Q., Elordi, A.G., Escondrillas, A.A. & De Artaza, D.L.D.I.G. (2021). A systematic literature review of artificial intelligence in fashion retail B2C. In 2021 6th international conference on smart and sustainable technologies (SpliTech) (pp. 01–06). IEEE

  5. Cheng, W.-H., Song, S., Chen, C.-Y., Hidayati, S. C., & Liu, J. (2021). Fashion meets computer vision: A survey. ACM Computing Surveys (CSUR), 54(4), 1–41.

    Article  Google Scholar 

  6. Chakraborty, S., Hoque, M.S., Rahman Jeem, N., Biswas, M.C., Bardhan, D., & Lobaton, E. (2021). Fashion recommendation systems, models and methods: A review. In Informatics (vol. 8, p. 49). MDPI.

  7. Landim, A., Pereira, A., Vieira, T., Costa, E.B., Moura, J., Wanick, V., & Bazaki, E. (2022). Chatbot design approaches for fashion e-commerce: An interdisciplinary review. International Journal of Fashion Design, Technology and Education, 15(2), 200–210.

    Article  Google Scholar 

  8. Zhang, Y., Zhang, C., & Du, F. (2019). A brief review of recent progress in fashion landmark detection. In 2019 12th International Congress on Image and Signal Processing, bioMedical Engineering and Informatics (CISP-BMEI) (pp. 1–6). IEEE.

  9. Chang, A.A., Ramadhan, J.F., Adnan, Z.K.S., Kanigoro, B., & Irwansyah, E. (2021). Fashion trend forecasting using machine learning techniques: A review. In Data science and intelligent systems: proceedings of 5th computational methods in systems and software 2021 (Vol. 2, pp. 34–44).

  10. Al-Halah, Z., Stiefelhagen, R., & Grauman, K. (2017). Fashion forward: Forecasting visual style in fashion. In Proceedings of the IEEE international conference on computer vision (pp. 388–397).

  11. Balim, C., & Özkan, K. (2023). Diagnosing fashion outfit compatibility with deep learning techniques. Expert Systems with Applications, 215, 119305.

    Article  Google Scholar 

  12. Cui, Y.R., Liu, Q., Gao, C.Y., & Su, Z. (2018). Fashiongan: display your fashion design using conditional generative adversarial nets. In Computer Graphics Forum (Vol. 37, pp. 109–119) . Wiley Online Library

  13. De Divitiis, L., Becattini, F., Baecchi, C., & Del Bimbo, A. (2021). Garment recommendation with memory augmented neural networks. In Proceedings of Pattern Recognition. ICPR international workshops and challenges: Virtual event, January 10–15, 2021, Part II (pp. 282–295). Springer.

  14. Deverall, J., Lee, J., & Ayala, M. (2017). Using generative adversarial networks to design shoes: The preliminary steps. CS231n in Stanford.

  15. Dong, J., Ma, Z., Mao, X., Yang, X., He, Y., Hong, R., & Ji, S. (2021). Fine-grained fashion similarity prediction by attribute-specific embedding learning. IEEE Transactions on Image Processing, 30, 8410–8425.

    Article  Google Scholar 

  16. Fenocchi, E., Morelli, D., Cornia, M., Baraldi, L., Cesari, F., & Cucchiara, R. (2022). Dual-branch collaborative transformer for virtual try-on. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2247–2251).

  17. Giri, C., & Chen, Y. (2022). Deep learning for demand forecasting in the fashion and apparel retail industry. Forecasting, 4(2), 565–581.

    Article  Google Scholar 

  18. Guan, C., Qin, S., & Long, Y. (2019). Apparel-based deep learning system design for apparel style recommendation. International Journal of Clothing Science and Technology, 31(3), 376–389.

    Article  Google Scholar 

  19. Hajjar, K., Lasserre, J., Zhao, A., & Shirvany, R. (2021). Attention gets you the right size and fit in fashion. In Recommender systems in fashion and retail (pp. 77–98). Springer

  20. Han, Y., Yang, S., Wang, W., & Liu, J. (2020). From design draft to real attire: Unaligned fashion image translation. In Proceedings of the 28th ACM international conference on multimedia (pp. 1533–1541).

  21. Hao, L., & Hao, M. (2019). Design of intelligent clothing selection system based on neural network. In 2019 IEEE 3rd information technology, networking, electronic and automation control conference (ITNEC) (pp. 1789–1792). IEEE.

  22. Hsiao, S.-W., Lee, C.-H., Chen, R.-Q., & Yen, C.-H. (2017). An intelligent system for fashion colour prediction based on fuzzy c-means and gray theory. Color Research & Application, 42(2), 273–285.

    Article  Google Scholar 

  23. Jiang, S., Li, J., & Fu, Y. (2021). Deep learning for fashion style generation. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4538–4550.

    Article  Google Scholar 

  24. Jo, J., Lee, S., Lee, C., Lee, D., & Lim, H. (2020). Development of fashion product retrieval and recommendations model based on deep learning. Electronics, 9(3), 508.

    Article  Google Scholar 

  25. Kang, W.-C., Fang, C., Wang, Z., & McAuley, J. (2017). Visually-aware fashion recommendation and design with generative image models. In 2017 IEEE international conference on data mining (ICDM) (pp. 207–216). IEEE.

  26. Kato, N., Osone, H., Sato, D., Muramatsu, N., & Ochiai, Y. (2018). Deepwear: A case study of collaborative design between human and artificial intelligence. In Proceedings of the twelfth international conference on tangible, embedded, and embodied interaction (pp. 529–536).

  27. Kato, N., Osone, H., Oomori, K., Ooi, C.W., & Ochiai, Y. Gans-based clothes design: Pattern maker is all you need to design clothing. In Proceedings of the 10th augmented human international conference 2019, pp. 1–7

  28. Kim, H. J., Lee, D. H., Niaz, A., Kim, C. Y., Memon, A. A., & Choi, K. N. (2021). Multiple-clothing detection and fashion landmark estimation using a single-stage detector. IEEE Access, 9, 11694–11704.

    Article  Google Scholar 

  29. Kuang, Z., Zhang, X., Yu, J., Li, Z., & Fan, J. (2021). Deep embedding of concept ontology for hierarchical fashion recognition. Neurocomputing, 425, 191–206.

    Article  Google Scholar 

  30. Laohakangvalvit, T., Sripian, P., Miyatake, K., & Ohkura, M. (2022). A proposal of classification model for kawaii fashion styles in Japan using deep learning. In Human-computer interaction. Theoretical approaches and design methods: Thematic Area, HCI 2022, Held as Part of the 24th HCI international conference, HCII 2022, Virtual Event, June 26–July 1, 2022, Proceedings, Part I (pp. 450–461). Springer.

  31. Lee, G.H., Kim, S., & Park, C.K. (2022). Development of fashion recommendation system using collaborative deep learning. International Journal of Clothing Science and Technology (ahead-of-print)

  32. Leithardt, V. (2021). Classifying garments from fashion-MNIST dataset through CNNs. Advances in Science, Technology and Engineering Systems Journal, 6(1), 989–994.

    Article  Google Scholar 

  33. Li, X., Ye, Z., Zhang, Z., & Zhao, M. (2021). Clothes image caption generation with attribute detection and visual attention model. Pattern Recognition Letters, 141, 68–74.

    Article  Google Scholar 

  34. Lin, J., Song, X., Gan, T., Yao, Y., Liu, W., & Nie, L. (2021). Paintnet: A shape-constrained generative framework for generating clothing from fashion model. Multimedia Tools and Applications, 80, 17183–17203.

    Article  Google Scholar 

  35. Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & De Rijke, M. (2019). Improving outfit recommendation with co-supervision of fashion generation. In The World Wide Web conference (pp. 1095–1105).

  36. Lin, Y., Moosaei, M., & Yang, H. (2020). Outfitnet: Fashion outfit recommendation with attention-based multiple instance learning. In Proceedings of The Web conference 2020 (pp. 77–87).

  37. Liu, L., Zhang, H., Ji, Y., & Wu, Q. J. (2019). Toward AI fashion design: An attribute-GAN model for clothing match. Neurocomputing, 341, 156–167.

    Article  Google Scholar 

  38. Liu, X., Li, J., Wang, J., & Liu, Z. (2021). Mmfashion: An open-source toolbox for visual fashion analysis. In Proceedings of the 29th ACM international conference on multimedia (pp. 3755–3758).

  39. Vasileva, M.I., Plummer, B.A., Dusad, K., Rajpal, S., Kumar, R., & Forsyth, D. (2018). Learning type-aware embeddings for fashion compatibility. In Proceedings of the European conference on computer vision (ECCV) (pp. 390–405).

  40. Lu, Z., Hu, Y., Chen, Y., & Zeng, B. (2021). Personalized outfit recommendation with learnable anchors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 12722–12731).

  41. Luo, J. (2020). Comparison of different models for clothing images classification studies. In Proceedings of the 2nd international conference on artificial intelligence and advanced manufacture (pp. 13–17).

  42. Ma, Y., Ding, Y., Yang, X., Liao, L., Wong, W.K., & Chua, T.-S. (2020). Knowledge enhanced neural fashion trend forecasting. In Proceedings of the 2020 international conference on multimedia retrieval (pp. 82–90).

  43. Mallavarapu, T., Cranfill, L., Kim, E. H., Parizi, R. M., Morris, J., & Son, J. (2021). A federated approach for fine-grained classification of fashion apparel. Machine Learning with Applications, 6, 100118.

    Article  Google Scholar 

  44. Mohammadi, S.O., Bodaghi, H., & Kalhor, A. Single-item fashion recommender: Towards cross-domain recommendations. In 2022 30th international conference on electrical engineering (ICEE) (pp. 12–16) (2022). IEEE.

  45. Moosaei, M., Lin, Y., Akhazhanov, A., Chen, H., Wang, F., & Yang, H. (2022). Outfitgan: Learning compatible items for generative fashion outfits. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2273–2277).

  46. Papadopoulos, S.-I., Koutlis, C., Papadopoulos, S., & Kompatsiaris, I. (2023). Victor: Visual incompatibility detection with transformers and fashion-specific contrastive pre-training. Journal of Visual Communication and Image Representation 103741

  47. Pecenakova, S., Karessli, N & Shirvany, R. (2022). Fitgan: Fit-and shape-realistic generative adversarial networks for fashion. In 2022 26th international conference on pattern recognition (ICPR) (pp. 3097���3104). IEEE.

  48. Revanur, A., Kumar, V., & Sharma, D. (2021). Semi-supervised visual representation learning for fashion compatibility. In Proceedings of the 15th ACM conference on recommender systems (pp. 463–472).

  49. Rohrmanstorfer, S., Komarov, M., & Mödritscher, F. (2021). Image classification for the automatic feature extraction in human worn fashion data. Mathematics, 9(6), 624.

    Article  Google Scholar 

  50. Samia, B., Soraya, Z., & Malika, M. (2022). Fashion images classification using machine learning, deep learning and transfer learning models. In 2022 7th international conference on image and signal processing and their applications (ISPA) (pp. 1–5). IEEE.

  51. Saranya, M., & Geetha, P. (2022). Fashion image classification using deep convolution neural network. In Computer, communication, and signal processing: 6th IFIP TC 5 international conference, ICCCSP 2022, Chennai, India, February 24–25, 2022, Revised Selected Papers, pp. 116–127 . Springer.

  52. Sbai, O., Elhoseiny, M., Bordes, A., LeCun, Y., & Couprie, C. (2018). Design: Design inspiration from generative networks. In Proceedings of the European conference on computer vision (ECCV) workshops

  53. Shajini, M., & Ramanan, A. (2022). A knowledge-sharing semi-supervised approach for fashion clothes classification and attribute prediction. The Visual Computer, 38(11), 3551–3561.

    Article  Google Scholar 

  54. Tautkute, I., Trzciński, T., Skorupa, A. P., Brocki, Ł, & Marasek, K. (2019). Deepstyle: Multimodal search engine for fashion and interior design. IEEE Access, 7, 84613–84628.

    Article  Google Scholar 

  55. Tengli, N.S., & Nandyal, S. (2022). Classification of fashion items based on shape features using machine learning. In Proceedings of international conference on computational intelligence, data science and cloud computing: IEM-ICDC 2021 (pp. 29–41). Springer.

  56. Wang, J., Cheng, X., Wang, R., & Liu, S. (2021). Learning outfit compatibility with graph attention network and visual-semantic embedding. In 2021 IEEE International conference on multimedia and expo (ICME) (pp. 1–6). IEEE.

  57. Werneck, H., Silva, N., Mito, C., Pereira, A., Tuler, E., Dias, D., & Rocha, L. (2022). A stacking recommender system based on contextual information for fashion retails. In Computational science and its applications–ICCSA 2022: 22nd international conference, Malaga, Spain, July 4–7, 2022, Proceedings, Part I. (pp. 560–574). Springer.

  58. Wu, Q., Zhu, B., Yong, B., Wei, Y., Jiang, X., Zhou, R., & Zhou, Q. (2021). ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks. Connection Science, 33(2), 341–358.

    Article  Google Scholar 

  59. Xhaferra, E., Cina, E., & Toti, L. (2022). Classification of standard fashion MNIST dataset using deep learning based CNN algorithms. In 2022 international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 494–498). IEEE.

  60. Yan, C., Zhou, L., & Wan, Y. (2019). A multi-task learning model for better representation of clothing images. IEEE Access, 7, 34499–34507.

    Article  Google Scholar 

  61. Yan, C., Li, Y., Wan, Y., & Zhang, Z. (2020). Joint image-text representation learning for fashion retrieval. In Proceedings of the 2020 12th international conference on machine learning and computing (pp. 412–417).

  62. Yang, X., Song, X., Han, X., Wen, H., Nie, J., & Nie, L. (2020). Generative attribute manipulation scheme for flexible fashion search. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 941–950).

  63. Yang, X., Song, X., Feng, F., Wen, H., Duan, L. .-Y. ., & Nie, L. . (2021). Attribute-wise explainable fashion compatibility modeling. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1), 1–21.

    Article  Google Scholar 

  64. Yu, C., Hu, Y., Chen, Y., & Zeng, B. (2019). Personalized fashion design. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9046–9055).

  65. Zhan, H., Lin, J., Ak, K. E., Shi, B., Duan, L.-Y., & Kot, A. C. (2021). A3-FKG: Attentive attribute-aware fashion knowledge graph for outfit preference prediction. IEEE Transactions on Multimedia, 24, 819–831.

    Article  Google Scholar 

  66. Zhao, L., Li, M., & Sun, P. (2021). Neo-fashion: A data-driven fashion trend forecasting system using catwalk analysis. Clothing and Textiles Research Journal, 0887302–211004299

  67. Zheng, H., Wu, K., Park, J.-H., Zhu, W., & Luo, J. (2021). Personalized fashion recommendation from personal social media data: An item-to-set metric learning approach. In 2021 IEEE International conference on big data (big data) (pp. 5014–5023). IEEE.

  68. Zhu, J., Yang, Y., Cao, J., & Mei, E.C.F. (2019). New product design with popular fashion style discovery using machine learning. In Artificial intelligence on fashion and textiles: proceedings of the artificial intelligence on fashion and textiles (AIFT) conference 2018, Hong Kong, July 3–6, 2018, pp. 121–128 . Springer.

  69. Zhu, D., Lai, X., & Rau, P.-L.P. (2022). Recognition and analysis of kawaii style for fashion clothing through deep learning. Human-Intelligent Systems Integration, 4(1–2), 11–22.

    Article  Google Scholar 

  70. McAuley, J., Targett, C., Shi, Q., & Van Den Hengel, A. (2015). Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval (pp. 43–52).

  71. Chen, H., Gallagher, A., & Girod, B. (2012). Describing clothing by semantic attributes. In Proceedings of the computer vision–ECCV 2012: 12th European conference on computer vision, Florence, Italy, October 7–13, 2012, , Part III 12, pp. 609–623 . Springer.

  72. Huang, J., Feris, R.S., Chen, Q., & Yan, S. (2015). Cross-domain image retrieval with a dual attribute-aware ranking network. In Proceedings of the IEEE international conference on computer vision (pp. 1062–1070).

  73. Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X. (2016). Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1096–1104).

  74. Ge, Y., Zhang, R., Wang, X., Tang, X., & Luo, P. (2019). Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5337–5345).

  75. Morelli, D., Fincato, M., Cornia, M., Landi, F., Cesari, F., & Cucchiara, R. (2022). Dress code: High-resolution multi-category virtual try-on. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2231–2235).

  76. Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & De Rijke, M. (2019). Explainable outfit recommendation with joint outfit matching and comment generation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1502–1516.

    Article  Google Scholar 

  77. Loni, B., Cheung, L.Y., Riegler, M., Bozzon, A., Gottlieb, L., & Larson, M. (2014). Fashion 10000: An enriched social image dataset for fashion and clothing. In Proceedings of the 5th ACM multimedia systems conference (pp. 41–46).

  78. Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., & Van Gool, L. (2013). Apparel classification with style. In Computer vision–ACCV 2012: 11th Asian conference on computer vision, Daejeon, Korea, November 5–9, 2012, Revised Selected Papers, Part IV 11 (pp. 321–335). Springer

  79. Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747

  80. Simo-Serra, E., Fidler, S., Moreno-Noguer, F., & Urtasun, R. (2015). Neuroaesthetics in fashion: Modeling the perception of fashionability. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 869–877).

  81. Zou, X., Kong, X., Wong, W., Wang, C., Liu, Y., & Cao, Y. (2019). Fashionai: A hierarchical dataset for fashion understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops.

  82. Yamaguchi, K., Kiapour, M.H., Ortiz, L.E., & Berg, T.L. (2012). Parsing clothing in fashion photographs. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3570–3577). IEEE.

  83. Song, X., Feng, F., Liu, J., Li, Z., Nie, L., & Ma, J. (2017). Neurostylist: Neural compatibility modeling for clothing matching. In Proceedings of the 25th ACM international conference on multimedia (pp. 753–761).

  84. Mall, U., Matzen, K., Hariharan, B., Snavely, N., & Bala, K. (2019). Geostyle: Discovering fashion trends and events. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 411–420)

  85. Chen, W., Huang, P., Xu, J., Guo, X., Guo, C., Sun, F., Li, C., Pfadler, A., Zhao, H., & Zhao, B. Pog: personalized outfit generation for fashion recommendation at Alibaba iFashion. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2662–2670 (2019)

  86. Guo, S., Huang, W., Zhang, X., Srikhanta, P., Cui, Y., Li, Y., Adam, H., Scott, M.R., & Belongie, S. (2019). The imaterialist fashion attribute dataset. In Proceedings of the IEEE/CVF international conference on computer vision workshops.

  87. Song, X., Han, X., Li, Y., Chen, J., Xu, X.-S., & Nie, L. (2019). Gp-bpr: Personalized compatibility modeling for clothing matching. In Proceedings of the 27th ACM international conference on multimedia (pp. 320–328)

  88. Liang, X., Gong, K., Shen, X., & Lin, L. (2018). Look into person: Joint body parsing and pose estimation network and a new benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 871–885.

    Article  Google Scholar 

  89. Yoo, D., Kim, N., Park, S., Paek, A.S., & Kweon, I.S. (2016). Pixel-level domain transfer. In Computer vision–ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VIII 14 (pp. 517–532). Springer

  90. Misra, R., Wan, M., & McAuley, J. (2018). Decomposing fit semantics for product size recommendation in metric spaces. In Proceedings of the 12th ACM conference on recommender systems (pp. 422–426).

  91. Han, X., Wu, Z., Jiang, Y.-G., & Davis, L.S. (2017). Learning fashion compatibility with bidirectional LSTMs. In Proceedings of the 25th ACM international conference on multimedia (pp. 1078–1086).

  92. Lu, Z., Hu, Y., Jiang, Y., Chen, Y., & Zeng, B. (2019). Learning binary code for personalized fashion recommendation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10562–10570)

  93. Gu, X., Wong, Y., Peng, P., Shou, L., Chen, G., & Kankanhalli, M.S. (2017). Understanding fashion trends from street photos via neighbor-constrained embedding learning. In Proceedings of the 25th ACM international conference on multimedia (pp. 190–198)

  94. Ak, K.E., Kassim, A.A., Lim, J.H., & Tham, J.Y. (2018). Learning attribute representations with localization for flexible fashion search. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7708–7717)

  95. Hadi Kiapour, M., Han, X., Lazebnik, S., Berg, A.C., & Berg, T.L. (2015). Where to buy it: Matching street clothing photos in online shops. In Proceedings of the IEEE international conference on computer vision (p. 3343–3351)

  96. He, R., & McAuley, J. (2016). VBPR: visual Bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30).

  97. Yu, A., & Grauman, K. (2014). Fine-grained visual comparisons with local learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 192–199)

  98. Han, X., Wu, Z., Wu, Z., Yu, R., & Davis, L.S. (2018). Viton: an image-based virtual try-on network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7543–7552)

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Acknowledgements

This work has received funding under grant agreement “eTryOn - virtual try-ons of garments enabling novel human fashion interactions” from the European Union’s Horizon 2020 research and innovation programme, no 951908.

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  1. Evridiki Papachristou, Thanos G. Stavropoulos, Anastasios Papazoglou Chalikias, Elisavet Chatzilari, Spiros Nikolopoulos and Ioannis Kompatsiaris have contributed equally to this work.

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  1. Centre for Research and Technology Hellas, 6th klm Charilaou - Thermi Road, Thessaloniki, Greece

    Elias Kouslis, Thanos G. Stavropoulos, Anastasios Papazoglou Chalikias, Elisavet Chatzilari, Spiros Nikolopoulos & Ioannis Kompatsiaris

  2. Creative Design and Clothing Department, International Hellenic University, 3rd km Kilkis Metalliko, Kilkis, Greece

    Evridiki Papachristou

  3. Abotis Hellas PC, Thermaikou 35 and Aigaiou, Thessaloniki, Greece

    Thanos G. Stavropoulos

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Kouslis, E., Papachristou, E., Stavropoulos, T.G. et al. AI in fashion: a literature review. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09872-z

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