Abstract
The objective of garment fashion trend prediction is to capture the trends of different garment attributes such as round necks and camouflage, enabling forecasts of their future popularity. Existing fashion trend prediction models have not sufficiently integrated comments on current social media networks and user preferences. Thus affecting the accuracy of garment popularity prediction. To address this issue, this paper proposes a fashion popularity prediction model based on multivariate attention fusion(MAFT). It combines diverse information posted by users on fashion platforms like Chictopia, uses GLU modules and dilated convolutions to preprocess multivariate features, enhances context feature extraction on sequence data, and suppresses irrelevant information. Subsequently, a multivariate attention fusion block is designed to capture the mapping relationship between dynamic and static variables in the input. After feature fusion, trend prediction for the garment is achieved through a Transformer layer. Experimental results demonstrate that this method accurately predicts future trends on the SFS, FIT, and Geo datasets, with improvements of 8.79% and 11.77% in MAE and MAPE evaluation metrics, respectively, compared to the best existing fashion trends prediction models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114–2124 (2021)
Ye, Y., Lu, J.: A fusion transformer for multivariable time series forecasting: the mooney viscosity prediction case. Entropy 24(4), 528 (2022)
Kiapour, M.H., Yamaguchi, K., Berg, A.C., Berg, T.L.: Hipster wars: discovering elements of fashion styles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 472–488. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_31
Jia, M., et al.: Fashionpedia: ontology, segmentation, and an attribute localization dataset. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 316–332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_19
Zou, X., Pang, K., Zhang, W., Wong, W.: How good is aesthetic ability of a fashion model? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21200–21209 (2022)
Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018)
Dong, H., et al.: Towards multi-pose guided virtual try-on network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9026–9035 (2019)
Hsieh, C.-W., Chen, C.-Y., Chou, C.-L., Shuai, H.-H., Liu, J., Cheng, W.-H.: Fashionon: semantic-guided image-based virtual try-on with detailed human and clothing information. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 275–283 (2019)
Al-Halah, Z., Grauman, K.: From Paris to Berlin: discovering fashion style influences around the world. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10136–10145 (2020)
Hsiao, W.-L., Grauman, K.: From culture to clothing: discovering the world events behind a century of fashion images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1066–1075 (2021)
Yu, H.-F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Tokgöz, A., Ünal, G.: A RNN based time series approach for forecasting Turkish electricity load. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE (2018)
Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Wu, N., Green, B., Ben, X., O’Banion, S.: Deep transformer models for time series forecasting: the influenza prevalence case. arXiv preprint arXiv:2001.08317 (2020)
van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Salinas, D., Valentin, F., Jan, G., Tim, J.: Deepar: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181–1191 (2020)
Hubert Tsai, Y.-H., Bai, S., Pu Liang, P., Zico Kolter, J., Morency, L.-P., Salakhutdinov, R.: Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the conference. Association for Computational Linguistics. Meeting, vol. 2019, pp. 6558. NIH Public Access (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Gu, X., Wong, Y., Peng, P., Shou, L., Chen, G., Kankanhalli, M.S.: Understanding fashion trends from street photos via neighbor-constrained embedding learning. In: Proceedings of the 25th ACM international conference on Multimedia, pp. 190–198 (2017)
Ma, Y., Ding, Y., Yang, X., Liao, L., Wong, W.K., Chua, T.-S.: Knowledge enhanced neural fashion trend forecasting. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 82–90 (2020)
Mall, U., Matzen, K., Hariharan, B., Snavely, N., Bala, K.: Geostyle: discovering fashion trends and events. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 411–420 (2019)
Christopher, A.: Sims. Macroeconomics and reality. Econometrica 48, 1–48 (1980)
Acknowledgements
Chen’s research was sponsored by the National Natural Science Foundation of China(Grant No.62202345).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, J., Zhao, Y., Zhong, S., Hu, X. (2024). Fashion Trend Forecasting Based on Multivariate Attention Fusion. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-8132-8_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8131-1
Online ISBN: 978-981-99-8132-8
eBook Packages: Computer ScienceComputer Science (R0)