Renovating Names in Open-Vocabulary Segmentation Benchmarks

• URL: http://arxiv.org/abs/2403.09593v2
• Date: Fri, 24 May 2024 07:57:33 GMT
• Title: Renovating Names in Open-Vocabulary Segmentation Benchmarks
• Authors: Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger,
• Abstract summary: We present 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. We show that our renovated names help train stronger open-vocabulary models with up to 15% relative improvement.
• Score: 31.243790558954288
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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