AI RESEARCH
Efficient Domain-Adaptive Multi-Task Dense Prediction with Vision Foundation Models
arXiv CS.CV
•
ArXi:2509.23626v2 Announce Type: replace Multi-task dense prediction, which aims to jointly solve tasks like semantic segmentation and depth estimation, is crucial for robotics applications but suffers from domain shift when deploying models in new environments. While unsupervised domain adaptation (UDA) addresses this challenge for single tasks, existing multi-task UDA methods primarily rely on adversarial learning approaches that are less effective than recent self-