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Demystifying Unsupervised Semantic Correspondence Estimation

  • Published:2022 Issue: Volume: Page:125-142
  • ISSN:0302-9743
  • Container-title:Lecture Notes in Computer Science
  • language:
  • Short-container-title:

Author:

Aygün Mehmet,Mac Aodha Oisin

Publisher

Springer Nature Switzerland

Reference78 articles.

1. Lecture Notes in Computer Science;H Alwassel,2018

2. Amir, S., Gandelsman, Y., Bagon, S., Dekel, T.: Deep ViT features as dense visual descriptors. arXiv:2112.05814 (2021)

3. Araslanov, N., Schaub-Meyer, S., Roth, S.: Dense unsupervised learning for video segmentation. In: NeurIPS (2021)

4. Banik, P., Li, L., Dong, X.: A novel dataset for keypoint detection of quadruped animals from images. arXiv:2108.13958 (2021)

5. Lecture Notes in Computer Science;B Biggs,2020

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2. Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

3. PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

4. ASIC: Aligning Sparse in-the-wild Image Collections;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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