O-MaMa @ EgoExo4D Correspondence Challenge: Learning Object Mask Matching between Egocentric and Exocentric Views

The goal of the correspondence task is to segment specific objects across different views. This technical report re-defines cross-image segmentation by treating it as a mask matching task. Our method consists of: (1) A Mask-Context Encoder that pools dense DINOv2 semantic features to obtain discriminative object-level representations from FastSAM mask candidates, (2) an Ego$\leftrightarrow$Exo Cross-Attention that fuses multi-perspective observations, (3) a Mask Matching contrastive loss that aligns cross-view features in a shared latent space, and (4) a Hard Negative Adjacent Mining strategy to encourage the model to better differentiate between nearby objects.

@article{mur-labadia2025_2506.06026,
  title={ O-MaMa @ EgoExo4D Correspondence Challenge: Learning Object Mask Matching between Egocentric and Exocentric Views },
  author={ Lorenzo Mur-Labadia and Maria Santos-Villafranca and Alejandro Perez-Yus and Jesus Bermudez-Cameo and Ruben Martinez-Cantin and Jose J. Guerrero },
  journal={arXiv preprint arXiv:2506.06026},
  year={ 2025 }
}