Papers › Open-Vocabulary Universal Image Segmentation with MaskCLIP

Open-Vocabulary Universal Image Segmentation with MaskCLIP

18 Aug 2022arXiv:2208.08984archive 2025-07-28

Zheng Ding, Jieke Wang, Zhuowen Tu

In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time. We first build a baseline method by directly adopting pre-trained CLIP models without finetuning or distillation. We then develop MaskCLIP, a Transformer-based approach with a MaskCLIP Visual Encoder, which is an encoder-only module that seamlessly integrates mask tokens with a pre-trained ViT CLIP model for semantic/instance segmentation and class prediction. MaskCLIP learns to efficiently and effectively utilize pre-trained partial/dense CLIP features within the MaskCLIP Visual Encoder that avoids the time-consuming student-teacher training process. MaskCLIP outperforms previous methods for semantic/instance/panoptic segmentation on ADE20K and PASCAL datasets. We show qualitative illustrations for MaskCLIP with online custom categories. Project website: https://maskclip.github.io.

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Tasks

Image SegmentationInstance SegmentationOpen Vocabulary Panoptic SegmentationOpen Vocabulary Semantic SegmentationPanoptic SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Open Vocabulary Semantic Segmentation ADE20K-150 MaskCLIP mIoU 23.7 #18 of 23 Archive leaderboard report
Open Vocabulary Semantic Segmentation ADE20K-847 MaskCLIP mIoU 8.2 #18 of 19 Archive leaderboard report
Open Vocabulary Semantic Segmentation PASCAL Context-459 MaskCLIP mIoU 10 #15 of 15 Archive leaderboard report
Open Vocabulary Semantic Segmentation PASCAL Context-59 MaskCLIP mIoU 45.9 #18 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIP

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