Meet SegGPT: a generalist model that performs arbitrary segmentation tasks on images or videos using inference in context

In computer vision, which seeks to locate and rearrange meaningful notions at the pixel level, such as foreground, category, object instance, etc., segmentation is one of the most fundamental challenges. For a variety of segmentation tasks, including foreground segmentation, interactive segmentation, semantic segmentation, instance segmentation, and panoptic segmentation, they have made considerable strides in recent years. These expert segmentation models, however, are restricted to particular tasks, classifications, granularities, data formats, etc. A new model should be trained when it adapts to a new environment, such as segmenting a new notion or objects in videos instead of images.

In this study, your goal 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 difficulties lie in two areas: (1) incorporating the many different types of data into the training, such as part, semantics, instance, panopticon, person, medical image, aerial image, etc.; and (2) create a generalizable training framework that differs from traditional multitasking learning, is flexible in defining tasks, and can handle tasks that are out of its reach. To overcome these problems, researchers from the Beijing Academy, Zhejiang University, and Peking University present SegGPT, a generalist paradigm for segmenting anything in context.

They integrate many segmentation tasks into a generalist contextual learning framework and view segmentation as a generic format for visual perception. This framework can handle multiple types of slicer data by converting them to the same image format. Using a random color mapping for each data sample, the SegGPT training problem is expressed as a coloring-in-context problem. The goal is to color only the associated areas, such as classes, object instances, components, etc., depending on the context. By employing a random color scheme, the model is forced to query contextual data to execute the given job instead of relying on certain hues. This allows training to be approached in a more adaptable and generic way.

The remaining training components remain the same when using a standard ViT and a smooth and easy l1 loss. After training, SegGPT can use in-context inference to perform various segmentation tasks on images or videos given some instances, such as object instance, things, slice, outline, text, etc. They suggest a simple but powerful context set technique, the featured set, which can help the model take advantage of the multi-example suggestion scenario. By tailoring a custom indicator for a specialized use case, such as ADE20K semantic segmentation in the domain, SegGPT can also easily function as a specialized model without modifying model parameters.

These are his main contributions.

(1) For the first time, they show a single generalist model that can automatically complete a wide range of segmentation tasks.

(2) For various tasks, such as few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation, they evaluate the pretrained SegGPT directly, that is, without fine tuning.

(3) Both subjectively and statistically, their results show great abilities to segment targets inside and outside the domain. However, their study does not promise to achieve new cutting-edge results or surpass existing specialized approaches across all benchmarks, as they believe that a general-purpose model may not be able to handle certain tasks.

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