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.
Latex Bibtex Citation:
@inproceedings{Huang2024NEURIPS,
  author = {Haiwen Huang and Songyou Peng and Dan Zhang and Andreas Geiger},
  title = {Renovating Names in Open-Vocabulary Segmentation Benchmarks},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024}
}

Abstract: We address the task of open-world class-agnostic object detection, i.e., detecting every object in an image by learning from a limited number of base object classes. State-of-the-art RGB-based models suffer from overfitting the training classes and often fail at detecting novel-looking objects. This is because RGB-based models primarily rely on appearance similarity to detect novel objects and are also prone to overfitting short-cut cues such as textures and discriminative parts. To address these shortcomings of RGB-based object detectors, we propose incorporating geometric cues such as depth and normals, predicted by general-purpose monocular estimators. Specifically, we use the geometric cues to train an object proposal network for pseudo-labeling unannotated novel objects in the training set. Our resulting Geometry-guided Open-world Object Detector (GOOD) significantly improves detection recall for novel object categories and already performs well with only a few training classes. Using a single "person" class for training on the COCO dataset, GOOD surpasses SOTA methods by 5.0% AR@100, a relative improvement of 24%.
Latex Bibtex Citation:
@inproceedings{Huang2023ICLR,
  author = {Haiwen Huang and Andreas Geiger and Dan Zhang},
  title = {GOOD: Exploring geometric cues for detecting objects in an open world},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023}
}