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Recognizing art work image from natural type: a deep adaptive depiction fusion method

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Abstract

As the big difference between natural type and art type, recognizing visual objects from photos to art paintings, cartoon pictures, or sketches introduces a great challenge. Domain adaptation focuses on overcoming the differences between different fields. It is an effective technology to bridge the cross-domain discrepancy by transferable features, while the existing domain-adaptive methods all need target domain images of the same category as source domain images to reduce domain shifts, which leads to limitations on target domain images. To solve this problem, we constructed an end-to-end unsupervised model called adaptive depiction fusion network (ADFN). Compared with other domain adaption methods, ADFN recognizes visual objects in art works by using only their natural type. It reinforces adaptive instance normalization technology to embed the depiction offset into the source domain features. At the meantime, we also provide a complete benchmark, cross-depiction-net, which is large and various enough to overcome the lack of data for this problem. To properly evaluate the performance of the ADFN, we compared it to different state-of-the-art methods (DAN, DDC, Deep-coral, and MRAN) on cross-depiction-net dataset. The results show that our model is superior to the state-of-the-art methods.

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Notes

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61702214), Development Project of Jilin Province of China (No. 202008 01033GH), Jilin Provincial Key Laboratory of Big Date Intelligent Computing (No. 20180622002JC), and the Fundamental Research Funds for the Central University, JLU.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130000, China

    Lan Huang, Yuzhao Wang & Tian Bai

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130000, China

    Lan Huang, Yuzhao Wang & Tian Bai

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  1. Lan Huang
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  2. Yuzhao Wang
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Correspondence to Tian Bai.

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Huang, L., Wang, Y. & Bai, T. Recognizing art work image from natural type: a deep adaptive depiction fusion method. Vis Comput 37, 1221–1232 (2021). https://doi.org/10.1007/s00371-020-01995-2

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