标题：SegGPT: Segmenting Everything In Context
[Beijing Academy of Artificial Intelligence& Zhejiang University & Peking University]
简介：
我们提出了SegGPT，一个用于分割上下文中所有事物的通用模型。我们将各种分割任务统一到一个通用的上下文学习框架中，通过将不同类型的分割数据转化为相同的图像格式，来容纳这些数据。SegGPT的训练被表述为一个上下文中的着色问题，每个数据样本都有随机的颜色映射。其目的是根据上下文完成不同的任务，而不是依赖特定的颜色。
经过训练，SegGPT可以通过上下文推理在图像或视频中执行任意的分割任务，如物体实例、东西、部分、轮廓和文本。SegGPT在广泛的任务中进行了评估，包括少数照片的语义分割、视频物体分割、语义分割和全景分割。我们的结果显示，在对域内和域外目标进行分割时，无论从质量上还是从数量上都有很强的能力。
https://arxiv.org/pdf/2304.03284.pdf
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