Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels

Olaf Dünkel1, Thomas Wimmer1,2, Christian Theobalt2, Christian Rupprecht3, Adam Kortylewski1,4
1Max Planck Institute for Informatics, 2ETH Zurich, 3University of Oxford, 4University of Freiburg
Paper arXiv Code 🤗 Demo
Teaser qualitative Method simple
DIY-SC finds semantic correspondences for extreme appearance and shape changes. Feature refinement with filtered pseudo-labels brings significant improvements.

Abstract

Finding correspondences between semantically similar points across images and object instances is one of the everlasting challenges in computer vision. While large pre-trained vision models have recently been demonstrated as effective priors for semantic matching, they still suffer from ambiguities for symmetric objects or repeated object parts. We propose to improve semantic correspondence estimation via 3D-aware pseudo-labeling. Specifically, we train an adapter to refine off-the-shelf features with pseudo-labels obtained via 3D-aware chaining, filtering wrong labels through relaxed cyclic consistency, and 3D spherical prototype mapping constraints. While reducing the need for dataset specific annotations compared to prior work, we set a new state-of-the-art on SPair-71k by over 4% absolute gain and by over 7% against methods with similar supervision requirements. The generality of our proposed approach simplifies extension of training to other data sources, which we demonstrate in our experiments.

Method

Description of the image

Method overview. We use azimuth information to sample image pairs for which higher zero-shot performance can be expected (1). We then chain the pairwise predictions to get correspondences for larger viewpoint changes, where we reject matches that do not fulfill a relaxed cyclic consistency constraint (2). We further filter pseudo-labels by rejecting pairs that cannot be mapped to a similar location on a 3D spherical prototype (3). Finally, we use the resulting pseudo-labels to train an adapter in a supervised manner (4).

Results.

PCK results

Results for different PCK levels (per-image) on the SPair-71k and the AP-10k dataset.

Ablations.

Ablations

Ablations on SPair-71k. All introduced components bring a significant improvement.



Acknowledgments

Adam Kortylewski acknowledges support via his Emmy Noether Research Group funded by the German Research Foundation (DFG) under Grant No. 468670075. Thomas Wimmer is supported through the Max Planck ETH Center for Learning Systems.

BibTeX

@article{duenkel2025diysc,
    title = {Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels},
    author = {D{\"u}nkel, Olaf and Wimmer, Thomas and Theobalt, Christian and Rupprecht, Christian and Kortylewski, Adam},
    journal = {arXiv preprint arXiv:2506.05312},
    year = {2025}
  }