Names are essential to both human cognition and vision-language models. Open-vocabulary models utilize class names as text prompts to generalize to categories unseen during training. However, name qualities are often overlooked and lack sufficient precision in existing datasets. In this paper, we address this underexplored problem by presenting a framework for "renovating" names in open-vocabulary segmentation benchmarks (RENOVATE). Through human study, we demonstrate that the names generated by our model are more precise descriptions of the visual segments and hence enhance the quality of existing datasets by means of simple renaming. We further demonstrate that using our renovated names enables training of stronger open-vocabulary segmentation models. Using open-vocabulary segmentation for name quality evaluation, we show that our renovated names lead to up to 16% relative improvement from the original names on various benchmarks across various state-of-the-art models. We provide our code and relabelings for several popular segmentation datasets (ADE20K, Cityscapes, PASCAL Context) to the research community.

翻译：命名对于人类认知和视觉语言模型均至关重要。开放词汇模型利用类别名称作为文本提示，以泛化到训练中未见的类别。然而，现有数据集中名称的质量常被忽视且缺乏足够精确性。本文针对这一未被充分探索的问题，提出了一个用于"重塑"开放词汇分割基准中命名的框架（RENOVATE）。通过人类研究，我们证明模型生成的名称对视觉片段的描述更为精确，从而通过简单的重命名提升了现有数据集的质量。我们进一步证明，使用重塑后的名称能够训练出更强的开放词汇分割模型。通过将开放词汇分割用于名称质量评估，我们发现采用重塑名称在多个基准测试及多种先进模型上，相较原始名称最高可实现16%的相对性能提升。我们向研究社区提供代码及对多个主流分割数据集（ADE20K、Cityscapes、PASCAL Context）的重新标注。