Papers › The Missing Point in Vision Transformers for Universal Image Segmentation
The Missing Point in Vision Transformers for Universal Image Segmentation
Sajjad Shahabodini, Mobina Mansoori, Farnoush Bayatmakou, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi
Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, accurately classifying these masks, especially in the presence of ambiguous boundaries and imbalanced class distributions, remains an open challenge. In this work, we introduce ViT-P, a novel two-stage segmentation framework that decouples mask generation from classification. The first stage employs a proposal generator to produce class-agnostic mask proposals, while the second stage utilizes a point-based classification model built on the Vision Transformer (ViT) to refine predictions by focusing on mask central points. ViT-P serves as a pre-training-free adapter, allowing the integration of various pre-trained vision transformers without modifying their architecture, ensuring adaptability to dense prediction tasks. Furthermore, we demonstrate that coarse and bounding box annotations can effectively enhance classification without requiring additional training on fine annotation datasets, reducing annotation costs while maintaining strong performance. Extensive experiments across COCO, ADE20K, and Cityscapes datasets validate the effectiveness of ViT-P, achieving state-of-the-art results with 54.0 PQ on ADE20K panoptic segmentation, 87.4 mIoU on Cityscapes semantic segmentation, and 63.6 mIoU on ADE20K semantic segmentation. The code and pretrained models are available at: https://github.com/sajjad-sh33/ViT-P}{https://github.com/sajjad-sh33/ViT-P.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | ADE20K val | ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280, COCO_pretrain) | AP | 40.7 | #3 of 14 | Archive leaderboard | report |
| Instance Segmentation | ADE20K val | ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280) | AP | 37.8 | #6 of 14 | Archive leaderboard | report |
| Instance Segmentation | Cityscapes val | ViT-P (OneFormer, ConvNeXt-L, single-scale, 512x1024, Mapillary Vistas-pretrained) | AP | 49.0 | #1 of 17 | Archive leaderboard | report |
| Instance Segmentation | Cityscapes val | ViT-P (OneFormer, ConvNeXt-L, single-scale, 512x1024, Mapillary Vistas-pretrained) | mask AP | 49.0 | #1 of 17 | Archive leaderboard | report |
| Panoptic Segmentation | ADE20K val | ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280, COCO_pretrain) | PQ | 54.0 | #2 of 25 | Archive leaderboard | report |
| Panoptic Segmentation | ADE20K val | ViT-P (OneFormer, DiNAT-L, single-scale, 1280x1280) | PQ | 51.9 | #7 of 25 | Archive leaderboard | report |
| Panoptic Segmentation | Cityscapes val | ViT-P (OneFormer, InternImage-H) | AP | 50.6 | #1 of 37 | Archive leaderboard | report |
| Panoptic Segmentation | Cityscapes val | ViT-P (OneFormer, InternImage-H) | PQ | 70.8 | #1 of 37 | Archive leaderboard | report |
| Panoptic Segmentation | Cityscapes val | ViT-P (OneFormer, InternImage-H) | mIoU | 85.4 | #1 of 37 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (InternImage-H) | Params (M) | 1610 | #1 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (InternImage-H) | Validation mIoU | 63.6 | #1 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (OneFormer, InternImage-H) | Params (M) | 1400 | #7 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (OneFormer, InternImage-H) | Validation mIoU | 61.6 | #7 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (OneFormer, DiNAT-L) | Params (M) | 309 | #14 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | ViT-P (OneFormer, DiNAT-L) | Validation mIoU | 59.9 | #14 of 235 | Archive leaderboard | report |
| Semantic Segmentation | COCO (Common Objects in Context) | ViT-P (OneFormer, InternImage-H) | mIoU | 69.1 | #2 of 9 | Archive leaderboard | report |
| Semantic Segmentation | COCO (Common Objects in Context) | ViT-P (OneFormer, DiNAT-L) | mIoU | 68.8 | #4 of 9 | Archive leaderboard | report |
| Semantic Segmentation | COCO-Stuff test | ViT-P (InternImage-H) | mIoU | 53.5 | #2 of 21 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | ViT-P (InternImage-H) | mIoU | 87.4 | #1 of 99 | 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
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