Papers › SegGPT: Segmenting Everything In Context
SegGPT: Segmenting Everything In Context
Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang
We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Semantic Segmentation | COCO-20i (1-shot) | SegGPT (ViT) | Mean IoU | 56.1 | #3 of 85 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | COCO-20i (5-shot) | SegGPT (ViT) | Mean IoU | 67.9 | #1 of 81 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (1-shot) | SegGPT (ViT) | Mean IoU | 85.6 | #20 of 24 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | FSS-1000 (5-shot) | SegGPT (ViT) | Mean IoU | 89.3 | #9 of 22 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (1-Shot) | SegGPT (ViT) | Mean IoU | 83.2 | #1 of 105 | Archive leaderboard | report |
| Few-Shot Semantic Segmentation | PASCAL-5i (5-Shot) | SegGPT (ViT) | Mean IoU | 89.8 | #1 of 96 | 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.
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