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Intelligent design of multimedia content in Alibaba

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Multimedia content is an integral part of Alibaba’s business ecosystem and is in great demand. The production of multimedia content usually requires high technology and much money. With the rapid development of artificial intelligence (AI) technology in recent years, to meet the design requirements of multimedia content, many AI auxiliary tools for the production of multimedia content have emerged and become more and more widely used in Alibaba’s business ecology. Related applications include mainly auxiliary design, graphic design, video generation, and page production. In this report, a general pipeline of the AI auxiliary tools is introduced. Four representative tools applied in the Alibaba Group are presented for the applications mentioned above. The value brought by multimedia content design combined with AI technology has been well verified in business through these tools. This reflects the great role played by AI technology in promoting the production of multimedia content. The application prospects of the combination of multimedia content design and AI are also indicated.

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  1. Azadi S, Fisher M, Kim VG, et al., 2018. Multi-content GAN for few-shot font style transfer. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.7564–7573.

  2. Bradski G, Kaehler A, 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Inc.

  3. Bretan M, Weinberg G, Heck L, 2016. A unit selection methodology for music generation using deep neural networks. https://doi.org/1612.03789

  4. Cao Z, Simon T, Wei SE, et al., 2017. Realtime multi-person 2D pose estimation using part affinity fields. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.7291–7299. https://doi.org/10.1109/CVPR.2017.143

  5. Chen LC, Zhu YK, Papandreou G, et al., 2018. Encoderdecoder with atrous separable convolution for semantic image segmentation. https://doi.org/1802.02611

  6. Chollet F, 2017. Xception: deep learning with depthwise separable convolutions. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1251–1258. https://doi.org/10.1109/CVPR.2017.195

  7. Goodfellow IJ, Pouget-Abadie J, Mirza M, et al., 2014. Generative adversarial nets. Proc 27th Int Conf on Neural Information Processing Systems, p.2672–2680.

  8. He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770–778. https://doi.org/10.1109/CVPR.2016.90

  9. He KM, Gkioxari G, Dollàr P, et al., 2017. Mask R-CNN. Proc IEEE Int Conf on Computer Vision, p.2961–2969. https://doi.org/10.1109/ICCV.2017.322

  10. Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

  11. Huang X, Peng YX, 2019. TPCKT: two-level progressive cross-media knowledge transfer. IEEE Trans Multim, 21(11):2850–2862. https://doi.org/10.1109/TMM.2019.2911456

  12. Kim KS, Zhang DN, Kang MC, et al., 2013. Improved simple linear iterative clustering superpixels. IEEE Int Symp on Consumer Electronics, p.259–260. https://doi.org/10.1109/ISCE.2013.6570216

  13. Levin A, Lischinski D, Weiss Y, 2007. A closed-form solution to natural image matting. IEEE Trans Patt Anal Mach Intell, 30(2):228–242. https://doi.org/10.1109/TPAMI.2007.1177

  14. Lin TY, Dollàr P, Girshick R, et al., 2017. Feature pyramid networks for object detection. Proc Conf on Computer Vision and Pattern Recognition, p.2117–2125. https://doi.org/10.1109/CVPR.2017.106

  15. Ngiam J, Khosla A, Kim M, et al., 2011. Multimodal deep learning. Proc 28th Int Conf on Machine Learning, p.689–696.

  16. Papandreou G, Zhu T, Chen LC, et al., 2018. PersonLab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. https://doi.org/1803.08225

  17. Peng YX, Zhu WW, Zhao Y, et al., 2017. Cross-media analysis and reasoning: advances and directions. Front Inform Technol Electron Eng, 18(1):44–57. https://doi.org/10.1631/FITEE.1601787

  18. Peng YX, Huang X, Zhao YZ, 2018. An overview of cross-media retrieval: concepts, methodologies, benchmarks, and challenges. IEEE Trans Circ Syst Video Technol, 28(9):2372–2385. https://doi.org/10.1109/TCSVT.2017.2705068

  19. Peng YX, Qi JW, Huang X, 2019. Current research status and prospects on multimedia content understanding. J Comput Res Devel, 56(1):187–212 (in Chinese). https://doi.org/10.7544/issn1000-1239.2019.20180770

  20. Ren SQ, He KM, Girshick R, et al., 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell, 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

  21. Ristani E, Tomasi C, 2018. Features for multi-target multicamera tracking and re-identification. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.6036–6046. https://doi.org/10.1109/CVPR.2018.00632

  22. Rother C, Kolmogorov V, Blake A, 2004. “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans Graph, 23(3):309–314. https://doi.org/10.1145/1015706.1015720

  23. Simonyan K, Zisserman A, 2015. Very deep convolutional networks for large-scale image recognition. https://doi.org/1409.1556

  24. Song SJ, Zhang W, Liu JY, et al., 2019. Unsupervised person image generation with semantic parsing transformation. https://doi.org/1904.03379

  25. Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Advances in Neural Information Processing Systems, p.5998–6008.

  26. Xia FT, Wang P, Chen XJ, et al., 2017. Joint multi-person pose estimation and semantic part segmentation. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.6769–6778. https://doi.org/10.1109/CVPR.2017.644

  27. Zhang SF, Zhu XY, Lei Z, et al., 2017. S3FD: single shot scale-invariant face detector. Proc IEEE Int Conf on Computer Vision, p.192–201. https://doi.org/10.1109/ICCV.2017.30

  28. Zhou BL, Khosla A, Lapedriza A, et al., 2016. Learning deep features for discriminative localization. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2921–2929. https://doi.org/10.1109/CVPR.2016.319

  29. Zhu JY, Park T, Isola P, et al., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf on Computer Vision, p.2223–2232. https://doi.org/10.1109/ICCV.2017.244

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Author information

Correspondence to Chang-yuan Yang.

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Compliance with ethics guidelines

Kui-long LIU, Wei LI, Chang-yuan YANG, and Guang YANG declare that they have no conflict of interest.

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Liu, K., Li, W., Yang, C. et al. Intelligent design of multimedia content in Alibaba. Front Inform Technol Electron Eng 20, 1657–1664 (2019). https://doi.org/10.1631/FITEE.1900580

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Key words

  • Multimedia content
  • Alibaba
  • Artificial intelligence
  • Design
  • Business application

CLC number

  • TP391