Going Denser with Open-Vocabulary Part Segmentation

Peize Sun, Shoufa Chen, Chenchen Zhu, Fanyi Xiao, Ping Luo, Saining Xie, Zhicheng Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs. First, we train the detector on the joint of part-level, object-level and image-level data to build the multi-granularity alignment between language and image. Second, we parse the novel object into its parts by its dense semantic correspondence with the base object. These two designs enable the detector to largely benefit from various data sources and foundation models. In open-vocabulary part segmentation experiments, our method outperforms the baseline by 3.3∼7.3 mAP in cross-dataset generalization on PartImageNet, and improves the baseline by 7.3 novel AP50 in cross-category generalization on Pascal Part. Finally, we train a detector that generalizes to a wide range of part segmentation datasets while achieving better performance than dataset-specific training.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages13
ISBN (Electronic)9798350307184
StatePublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, France
Duration: Oct 2 2023Oct 6 2023

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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