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SegGPT: Segmenting Everything In Context

Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang

arXiv:2304.03284v1cs.CV

TL;DR

SegGPT addresses the need for segmentation models that can handle diverse tasks, categories, and data types without retraining for each setting. It reformulates segmentation as in-context coloring over a shared image representation, using random color mappings to make predictions depend on context. The resulting generalist performs varied image and video segmentation tasks, with strong in-domain and out-of-domain capabilities, while not targeting state-of-the-art performance on every benchmark.

  • Problem

    Specialist segmentation models are limited to specific tasks, classes, granularities, and data types, so new settings require retraining and expensive annotation.

  • Method

    SegGPT unifies diverse segmentation data as images and trains an in-context coloring model with random color mappings, requiring task completion from contextual examples rather than fixed colors.

  • Results

    SegGPT performs diverse image and video segmentation tasks through in-context inference, including few-shot semantic, video object, semantic, and panoptic segmentation, with strong in-domain and out-of-domain capabilities.

  • Takeaways & Limitations

    A single generalist model can handle varied segmentation task definitions and targets through in-context inference, including tasks outside its training domain.

  • Takeaways & Limitations

    Random coloring improves generalization capability but makes training more difficult and may reduce performance on in-domain tasks with ample training data.

Abstract

from arXiv · show

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.

1. Introduction

SegGPT addresses the limits of specialist segmentation models with a single in-context generalist trained across diverse segmentation data. It supports varied image and video tasks, including in-domain and out-of-domain segmentation, without fine-tuning.

  • Motivation: Specialist segmentation models are restricted to particular tasks, classes, granularities, and data types, requiring retraining for new settings.The paper identifies annotation cost and poor sustainability as consequences of repeatedly adapting specialist models.
  • Motivation: SegGPT targets a single model capable of solving diverse and unlimited segmentation tasks across heterogeneous data types and out-of-domain settings.The stated challenges are incorporating part, semantic, instance, panoptic, person, medical, and aerial segmentation data while maintaining flexible task definitions.
  • Approach: SegGPT unifies segmentation tasks as in-context coloring, transforming data into image format and using random color mappings so predictions follow context rather than fixed colors.The training objective colors corresponding classes, instances, or parts according to contextual examples.
  • Capabilities: After training, SegGPT performs diverse image and video segmentation tasks from a few examples and can act as a specialist through prompt tuning without parameter updates.Supported examples include object instance, stuff, part, contour, text, and in-domain ADE20K semantic segmentation.
  • Evaluation: SegGPT is evaluated directly, without fine-tuning, on few-shot semantic, video object, semantic, and panoptic segmentation, with strong in-domain and out-of-domain results.The contributions emphasize broad evaluation and qualitative or quantitative capability across both target domains.
  • Scope: The paper does not claim new state-of-the-art results or superiority over specialist methods across all benchmarks.The authors frame this as outside the intended responsibility of a general-purpose model.

2. Related Work

Related work includes task-specific segmentation methods, vision generalists, and in-context visual learning frameworks. SegGPT extends the Painter-style image formulation with random coloring to preserve flexible task definitions and support out-of-domain segmentation.

  • Visual Segmentation: Segmentation methods have traditionally targeted specific tasks and cannot readily switch tasks or categories.The paper motivates a general interface compatible with diverse segmentation tasks.
  • Vision Generalists: Vision generalists unify tasks with Transformer-based models, including detection, instance segmentation, keypoint estimation, and captioning.Examples include DETR and the Pix2Seq series.
  • Vision Generalists: Hard indicators such as special tokens can make existing unified models difficult to generalize to new tasks.SegGPT instead maintains task-definition flexibility through in-context examples and random coloring.
  • In-Context Visual Learning: SegGPT builds on Painter by focusing its image-based in-context framework on segmentation and unifying semantic, instance, part, aerial, and other specialized data.The approach treats segmentation as a central visual-perception category while retaining a shared image representation.
  • In-Context Visual Learning: Random coloring forces contextual reference rather than a collapse into a conventional multi-task solution, while segmentation’s lower data variability supports shared internal structures.The paper links this design to maintaining out-of-domain generalization alongside effective in-domain training.

3. Approach

SegGPT turns diverse segmentation tasks into a unified in-context image-coloring framework, using randomized color mappings to make contextual examples determine the task. It supports single- and multi-example inference and prompt-based specialization without updating model parameters.

  • Unified framework: SegGPT reformulates segmentation as image inpainting, retaining a vanilla ViT and smooth-ℓ1 loss while introducing random coloring for generalization.Different task outputs are represented as images whose masked pixels are reconstructed.
  • Unified framework: Fixed task colors can make Painter rely on color identity instead of relationships between segments.This limitation motivates randomized color mappings during in-context training.
  • Multi-example inference: Multiple examples can be combined through spatial ensemble grids or feature ensemble averaging after each attention layer.Spatial ensemble stitches and resizes examples; feature ensemble lets the query aggregate all references.
  • In-context coloring: Random coloring remaps sampled target colors, while mix-context training stitches multiple same-mapping examples before cropping and resizing.The design encourages attention to contextual information rather than specific colors.
  • In-context coloring: SegGPT unifies segmentation datasets by varying sampling strategies: categories for semantic segmentation, object instances for instance segmentation, and augmented image views as context.In implementation, shared colors denote the same category or instance.
  • Inference and tuning: SegGPT supports arbitrary image or video segmentation from examples and can specialize through a learned prompt while freezing model parameters.Prompt tuning can target datasets, scenes, or characters, and visualizations include object, part, video, and close-set instance segmentation.

4. Experiment

SegGPT is evaluated as a single generalist model across diverse segmentation settings, including semantic, video object, and panoptic segmentation. It performs competitively across in-domain and out-of-domain tasks, while in-context tuning remains behind a generalist baseline on ADE20K and COCO panoptic segmentation.

  • Training Data: The training mixture spans part, semantic, instance, panoptic, person, retinal-vessel, and aerial-image segmentation datasets.The unified approach adds datasets without modifying the architecture or training pipeline.
  • Experimental Setup: SegGPT uses one ViT-L generalist model and evaluates it across multiple segmentation benchmarks without task-specific fine-tuning.The evaluated tasks include few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation.
  • Few-shot Semantic Segmentation: SegGPT achieves comparable or significantly better few-shot semantic-segmentation performance than recent specialist models on COCO-20i and PASCAL-5i.It also surpasses the generalist Painter by a considerable margin.
  • Few-shot Semantic Segmentation: SegGPT achieves highly competitive few-shot semantic-segmentation performance on out-of-domain FSS-1000 categories despite not being trained on FSS-1000.The evaluation compares it with specialist models trained on FSS-1000.
  • Video Object Segmentation: SegGPT achieves competitive video object segmentation results without task-specific training, outperforming AGAME and AGSS on YouTube-VOS 2018 and matching RDE comparably on challenging MOSE.It constructs in-context coloring examples from the first frame and prior predicted frames, then applies feature ensemble.
  • In-Context Tuning: In-context tuning makes SegGPT competitive with RefineNet on ADE20K, but it trails Painter by 10.3 mIoU points on ADE20K and 9.0 PQ points on COCO panoptic segmentation.The authors attribute these drops to the random color scheme making optimization more difficult.

5. Discussion and Conclusion

SegGPT demonstrates a generalist segmentation model that uses in-context visual learning to handle in-domain and out-of-domain tasks. The authors also identify a trade-off: random coloring improves generalization but can reduce performance on data-rich in-domain tasks.

  • 5. Discussion and Conclusion: SegGPT uses an in-context visual learning strategy to support diverse segmentation tasks with a generalist model.The conclusion emphasizes training strategy as central to leveraging flexibility in in-context visual learning.
  • 5. Discussion and Conclusion: SegGPT handles both in-domain and out-of-domain tasks, including object instance, stuff, part, contour, and text segmentation.These capabilities are reported across multiple segmentation settings.
  • 5. Discussion and Conclusion: Random coloring improves generalization capability but makes training inherently more difficult.The authors connect this difficulty to inferior performance on some in-domain tasks with ample training data.
  • 5. Discussion and Conclusion: Inferior performance may occur on ADE20K semantic segmentation and COCO panoptic segmentation when ample in-domain training data are available.The paper presents these as examples of the trade-off associated with the random coloring regime.
  • 5. Discussion and Conclusion: The authors identify scaling model size as a future direction for capturing more complex data patterns and potentially improving segmentation results.They note that larger models also introduce challenges that are not fully specified in the passage.

A. Additional Implementation Details

SegGPT is trained on a mixture of segmentation datasets spanning instance, semantic, panoptic, person, aerial, and retinal-vessel data.

  • A. Additional Implementation Details: Training samples come from datasets covering COCO instance, ADE20K semantic, COCO panoptic, Cityscapes, COCO stuff, person, aerial, and retinal-vessel segmentation.The listed dataset sampling weights range from 0.06 to 0.22.
  • A. Additional Implementation Details: COCO instance data receive the highest listed sampling weight at 0.22.ADE20K semantic and COCO panoptic semantic data each receive a sampling weight of 0.15.

B. Additional Results

Additional experiments show that more examples improve example-based semantic segmentation and that feature ensemble helps resolve ambiguous objects across video frames. The supplementary figures also visualize applications across semantic, instance, and arbitrary segmentation.

  • B. Additional Results: +13.1% mIoU is obtained when increasing example-based ADE20K prompts from 1 to 16 examples.The improvement is reported despite a remaining gap with tuned prompts.
  • B. Additional Results: Feature ensemble enables successful segmentation of individual objects in crowded video scenes using several previous frames and pseudo-labels.Using only the first annotated frame produces incorrect predictions for some crowd instances.
  • B. Additional Results: Supplementary visualizations include ADE20K semantic segmentation, COCO instance segmentation, and arbitrary segmentation in the wild.Figure S2 provides additional examples of these applications.
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