SegGPT: Towards Segmenting Everything In Context
Abstract
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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In-context learning models are computationally efficient and can adapt their predictions based on a small support set.
“Unlike previous approaches, that rely on training CNNs [29] or computationally expensive atlas-based registration methods as reverse classifiers [5], [28], in-context learning models are computationally efficient and can adapt their predictions based on a small support set.”
From ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality (2025) · cites this paper as [9]
Cites
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End-to-End Object Detection with Transformers
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“…witnessed great progress in developing more accurate and faster algorithms for various segmentation tasks, such as foreground segmentation [45], interactive segmentation [55, 38], semantic segmentation [36, 32, 58, 43], instance segmentation [20, 13, 4, 52], and panoptic segmentation [26, 7, 10].”
MethodSelf-citationThe Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
Vision Transformers for Dense Prediction
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“…witnessed great progress in developing more accurate and faster algorithms for various segmentation tasks, such as foreground segmentation [45], interactive segmentation [55, 38], semantic segmentation [36, 32, 58, 43], instance segmentation [20, 13, 4, 52], and panoptic segmentation [26, 7, 10].”
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A Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649 · see citing works
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.15161 · Cited by 115
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Cosarinsky, M., Billot, R., Mansilla, L., Jimenez, G., Gaggión, N., Fu, G., Tirer, T., & Ferrante, E. (2025) · arXiv
“To improve practicality and e ffi ciency, we incorporate In-Context RCA , which uses in-context learning segmentation models [9], [10], [11] that can perform new segmentation tasks by conditioning on a small set of labeled examples without requiring any additional training.”
- Authors
- Wang, X., Zhang, X., Cao, Y., Wang, W., Shen, C., & Huang, T.
- Affiliations
- Beijing Academy of Artificial Intelligence (CN)
- Zhejiang University (CN)
- Peking University (CN)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 1130-1140
- OpenAlex
- W4390873795
- Cited by
- 310
- References
- 9
- Citations by year
- 2026: 63 · 2025: 148 · 2024: 88 · 2023: 11
- FWCI
- 54.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- in-context learning, part segmentation, contour segmentation, text segmentation, video object segmentation, panoptic segmentation, few-shot semantic segmentation
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