Introducing SegGPT: A generalist model that performs arbitrary segmentation tasks in images or videos via in-context inference

Segmentation is one of the most fundamental challenges in computer vision, which seeks to find and reorganize important concepts such as foreground, categories, and object instances at the pixel level. We have made significant progress in recent years on various segmentation tasks such as foreground segmentation, interactive segmentation, semantic segmentation, instance segmentation, and panoptic segmentation. However, these expert segmentation models are limited to specific tasks, classifications, granularities, data formats, etc. New models need to be trained when adapting to new environments, such as segmenting new concepts or objects in videos instead of photos.

Our goal in this work is to train a single model that can handle an infinite variety of segmentation tasks. This requires time-consuming annotation work and should be more sustainable for many segmentation jobs. The main difficulty he has is in two areas. (1) Incorporating very different data types such as parts, semantics, instances, panoptics, people, medical images, aerial images into training. (2) create a generalizable training scheme that is flexible in task definition and can handle tasks outside its scope, unlike traditional multitask learning; To overcome these problems, researchers from the Beijing Academy of Sciences, Zhejiang University, and Peking University introduced his SegGPT, a general-purpose paradigm that segments anything in context.

They integrated many segmentation tasks into a general-purpose in-context learning framework and see segmentation as a popular form of visual recognition. This framework can handle different segmentation data types by converting them to the same image format. By using random color mapping for each data sample, the SegGPT training problem is expressed as an in-context coloring problem. The goal is to contextually color only relevant areas such as classes, object instances, and components. By employing a random color scheme, the model should reference contextual data to do a specific job, rather than relying on a particular hue. This allows you to approach your training in a more adaptable and versatile way.

When using standard ViT and simple smooth-l1 loss, the rest of the training components remain the same. Following training, SegGPT can use in-context inference to perform various segmentation tasks in images and videos given several instances of object instances, objects, parts, contours, text, etc. These are simple but powerful contextual ensemble techniques that help models exploit multiple example prompt scenarios. By tailoring customized prompts to special use cases such as intra-domain ADE20K semantic segmentation, SegGPT can also easily function as a specialized model without changing model parameters.

These are their main contributions.

(1) They demonstrated for the first time a single generalist model that can automatically complete a wide range of segmentation tasks.

(2) We evaluate pre-trained SegGPTs directly, i.e. without fine-tuning, for various tasks such as few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation.

(3) Both subjectively and statistically, their results show good skill in segmenting in- and out-of-domain targets. Nonetheless, their research believes that general-purpose models may not be able to handle certain tasks, thus achieving new state-of-the-art results or outperforming existing specialized approaches across all benchmarks. It does not promise to demonstrate performance.

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