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Rein++: Efficient Generalization and Adaptation for Semantic Segmentation With Vision Foundation Models.

Zhixiang Wei, Xiaoxiao Ma, Ruishen Yan

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 8, 2026

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Rein++ efficiently adapts Vision Foundation Models for semantic segmentation, overcoming data scale and domain shift challenges for improved generalization on limited, diverse datasets without target labels.

Area of Science:

Computer Vision
Machine Learning

Background:

Vision Foundation Models (VFMs) excel in computer vision but struggle with semantic segmentation due to small datasets and domain shifts.
Existing methods face challenges adapting large-scale pre-trained models to specific, often limited, segmentation tasks.

Purpose of the Study:

To introduce Rein++, an efficient framework for VFM-based semantic segmentation.
To address data scale disparity and domain distribution shifts in VFM segmentation.
To enable effective generalization from limited data and adaptation to diverse unlabeled scenarios.

Main Methods:

Rein++ combines domain generalization (Rein-G) and unsupervised domain adaptation (Rein-A).
Rein-G uses trainable, instance-aware tokens to refine VFM features, fine-tuning <1% of parameters for generalization.

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Rein-A employs instance and logit-level adaptation, plus a semantic transfer module leveraging the Segment Anything Model for boundary enhancement.

Main Results:

Rein++ demonstrates superior generalization from limited data and effective adaptation to diverse unlabeled domains.
The framework significantly outperforms state-of-the-art methods in VFM-based semantic segmentation.
Achieves efficient training, even for large VFM models with billions of parameters.

Conclusions:

Rein++ offers an efficient, generalizable, and adaptive solution for VFM semantic segmentation.
The framework successfully mitigates challenges of data scale and domain shift.
Enables robust segmentation performance across diverse scenarios without requiring target labels.