Discrete-Continuous Transformation Matching for Dense Semantic Correspondence

@article{Kim2020DiscreteContinuousTM,
  title={Discrete-Continuous Transformation Matching for Dense Semantic Correspondence},
  author={Seungryong Kim and Dongbo Min and Stephen Lin and Kwanghoon Sohn},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020},
  volume={42},
  pages={59-73}
}
  • Seungryong Kim, Dongbo Min, +1 author Kwanghoon Sohn
  • Published in
    IEEE Transactions on Pattern…
    2020
  • Computer Science, Medicine
  • Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there is a lack of practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework… CONTINUE READING

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    SHOWING 1-10 OF 83 REFERENCES

    Proposal Flow: Semantic Correspondences from Object Proposals

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL

    Joint Recovery of Dense Correspondence and Cosegmentation in Two Images

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    Proposal Flow

    VIEW 12 EXCERPTS
    HIGHLY INFLUENTIAL

    DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences

    VIEW 12 EXCERPTS
    HIGHLY INFLUENTIAL

    Deformable Spatial Pyramid Matching for Fast Dense Correspondences

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    Matching Local Self-Similarities across Images and Videos

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    One-shot learning of object categories

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL