# Renovating Names in Open-Vocabulary Segmentation Benchmarks

Source: [Renovating Names in Open-Vocabulary Segmentation Benchmarks](<https://www.sota2.com/research/paper/68fa734e1e209131cea5bb22>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:42:57.838Z

Authors: Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger

Published: 2024-03-14T17:35:32.000Z

## Abstract

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, the precision of these names is often overlooked in existing datasets. In this paper, we address this underexplored problem by presenting a framework for "renovating" names in open-vocabulary segmentation benchmarks (RENOVATE). Our framework features a renaming model that enhances the quality of names for each visual segment. Through experiments, we demonstrate that our renovated names help train stronger open-vocabulary models with up to 15% relative improvement and significantly enhance training efficiency with improved data quality. We also show that our renovated names improve evaluation by better measuring misclassification and enabling fine-grained model analysis. We will provide our code and relabelings for several popular segmentation datasets (MS COCO, ADE20K, Cityscapes) to the research community.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.09593>)

[PDF](<https://arxiv.org/pdf/2403.09593.pdf>)