O-MaMa @ EgoExo4D Correspondence Challenge: Learning Object Mask Matching between Egocentric and Exocentric Views
Journal:
arXiv
Published Date:
Jun 6, 2025
Abstract
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.
