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SegGPT: Segmenting Everything In Context

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arxiv 2304.03284 v1 pith:WRIRWEJ7 submitted 2023-04-06 cs.CV

Xinlong Wang , Xiaosong Zhang , Yue Cao , Wen Wang , Chunhua Shen , Tiejun Huang This is my paper
classification cs.CV
keywords segmentationseggpttaskscontextin-contextsegmentingdataeverything
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 6 Pith papers

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