In this work, we address in-context learning (ICL) for the task of image
segmentation, introducing a novel approach that adapts a modern Video Object
Segmentation (VOS) technique for visual in-context learning. This adaptation is
inspired by the VOS method's ability to efficiently and flexibly learn objects
from a few examples. Through evaluations across a range of support set sizes
and on diverse segmentation datasets, our method consistently surpasses
existing techniques. Notably, it excels with data containing classes not
encountered during training. Additionally, we propose a technique for support
set selection, which involves choosing the most relevant images to include in
this set. By employing support set selection, the performance increases for all
tested methods without the need for additional training or prompt tuning. The
code can be found at this https URL

通过对现代视频对象分割技术的可视化上下文学习方法进行改进，我们提出了一种用于图像分割的上下文学习方法，并通过在支持集大小和不同分割数据集上进行评估，验证了该方法在效果上超越现有技术，并在包含训练集未遇到的类别的数据上表现出色，另外我们还提出了一种支持集选择技术，通过选择最相关的图像来提高各种测试方法的性能，而无需进行额外的训练或提示调整。