Going Denser with Open-Vocabulary Part Segmentation

TL;DR

This paper introduces a novel detector capable of open-vocabulary object and part segmentation by leveraging multi-granularity data and dense semantic correspondence, significantly improving generalization across datasets and categories.

Contribution

The paper presents a new method that combines multi-level data training and dense semantic matching to enable open-vocabulary object and part segmentation.

Findings

Outperforms baseline by 3.3-7.3 mAP on PartImageNet

Improves baseline by 7.3 AP50 on Pascal Part

Generalizes well across various part segmentation datasets

Abstract

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$\sim$7.3 mAP in cross-dataset generalization on PartImageNet, and improves the baseline by 7.3 novel AP$_{50}$ 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.