Скачать или смотреть Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels, ICCV 2025.

Paper 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.

Reference Publication:
Olaf Dünkel, Thomas Wimmer, Christian Theobalt, Christian Rupprecht, Adam Kortylewski.
Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels, ICCV 2025.

Project Page: https://genintel.github.io/DIY-SC