Lost in Translation? Vocabulary Alignment for Source-Free Adaptation in Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2509.15225v3
• Date: Mon, 29 Sep 2025 12:25:06 GMT
• Title: Lost in Translation? Vocabulary Alignment for Source-Free Adaptation in Open-Vocabulary Semantic Segmentation
• Authors: Silvio Mazzucco, Carl Persson, Mattia Segu, Pier Luigi Dovesi, Federico Tombari, Luc Van Gool, Matteo Poggi,
• Abstract summary: VocAlign is a source-free domain adaptation framework specifically designed for VLMs in semantic segmentation.<n>Our approach achieves a notable 6.11 mIoU improvement on the CityScapes dataset and demonstrates superior performance on zero-shot segmentation benchmarks.
• Score: 90.5844979560448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce VocAlign, a novel source-free domain adaptation framework specifically designed for VLMs in open-vocabulary semantic segmentation. Our method adopts a student-teacher paradigm enhanced with a vocabulary alignment strategy, which improves pseudo-label generation by incorporating additional class concepts. To ensure efficiency, we use Low-Rank Adaptation (LoRA) to fine-tune the model, preserving its original capabilities while minimizing computational overhead. In addition, we propose a Top-K class selection mechanism for the student model, which significantly reduces memory requirements while further improving adaptation performance. Our approach achieves a notable 6.11 mIoU improvement on the CityScapes dataset and demonstrates superior performance on zero-shot segmentation benchmarks, setting a new standard for source-free adaptation in the open-vocabulary setting.

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