标题： 翻译中的迷失？ 用于开放词汇语义分割的无源域适应词汇对齐 显示英文标题

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

摘要： 我们引入了VocAlign，这是一种专为开放词汇语义分割中的视觉语言模型（VLMs）设计的新颖无源域适应框架。 我们的方法采用了一种结合词汇对齐策略的学生-教师范式，通过引入额外的类别概念来改进伪标签生成。 为了确保效率，我们使用低秩适应（LoRA）来微调模型，在保持其原始能力的同时最小化计算开销。 此外，我们为学生模型提出了一种Top-K类别选择机制，显著降低了内存需求，同时进一步提高了适应性能。 我们的方法在CityScapes数据集上实现了显著的6.11 mIoU提升，并在零样本分割基准测试中表现出色，为开放词汇设置下的无源适应设定了新标准。 显示英文摘要

摘要： 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.