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Cited by 47Generative Adversarial Networks and Image SynthesisImage Processing and 3D ReconstructionAdvanced Image and Video Retrieval Techniques

JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles With Generative Adversarial Networks

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Li, Ru, Liu, Shuaicheng, Wang, Guangfu, Liu, Guanghui, Zeng, Bing·2022·IEEE Transactions on Image Processing
DOI 10.1109/tip.2021.3120052
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Abstract

The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationships based on the boundaries of pieces, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based auxiliary learning method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, where the generator produces the natural images to fool the discriminator, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target domain. These two branches are connected by a flow-based warp module that is applied to warp features to correct the order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and boundary information simultaneously. Qualitative and quantitative comparisons against several representative jigsaw puzzle solvers demonstrate the superiority of our method.

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  • Li et al. solved jigsaw puzzles using a GAN model with a multitask pipeline for unpaired image jigsaw puzzles.

    “Li et al. [24] solved Jigsaw puzzles using the GAN model. They created a multitask pipeline for solving unpaired image jigsaw puzzles.”

    From Self-supervised learning: A succinct review (2023) · cites this paper as [24]

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Research works printed but not linked: 2

  1. [32][32] O. Vinyals, M. Fortunato, and N. Jaitly, 'Pointer networks,' in Proc. NIPS , vol. 28, pp. 2692-2700, 2015. -
  2. [38][38] M.-M. Paumard, D. Picard, and H. Tabia, 'Jigsaw puzzle solving using local feature co-occurrences in deep neural networks,' in in Proc. ICIP , pp. 1018-1022, 2018. -

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Authors
Li, R., Liu, S., Wang, G., Liu, G., & Zeng, B.
Affiliations
  • University of Electronic Science and Technology of China (CN)
  • Megvii (China) (CN)
ORCID
Venue
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
31
Pages
513-524
ISSN
  • 1057-7149 (ISSN-L)
  • 1941-0042
DOI
10.1109/tip.2021.3120052
OpenAlex
W4200132640
Cited by
47
References
37
Citations by year
2026: 6 · 2025: 12 · 2024: 12 · 2023: 10 · 2022: 6 · 2021: 1
FWCI
2.61
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Image Processing (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (61872067, 62071097, 62031009, 61720106004)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Processing and 3D Reconstruction
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
jigsaw puzzle solving, generative adversarial networks, auxiliary learning, pseudo-labeling, multi-task learning

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