In-context segmentation aims at segmenting novel images using a few labeled
example images, termed as "in-context examples", exploring content similarities
between examples and the target. The resulting models can be generalized
seamlessly to novel segmentation tasks, significantly reducing the labeling and
training costs compared with conventional pipelines. However, in-context
segmentation is more challenging than classic ones due to its meta-learning
nature, requiring the model to learn segmentation rules conditioned on a few
samples, not just the segmentation. Unlike previous work with ad-hoc or
non-end-to-end designs, we propose SEGIC, an end-to-end segment-in-context
framework built upon a single vision foundation model (VFM). In particular,
SEGIC leverages the emergent correspondence within VFM to capture dense
relationships between target images and in-context samples. As such,
information from in-context samples is then extracted into three types of
instructions, i.e. geometric, visual, and meta instructions, serving as
explicit conditions for the final mask prediction. SEGIC is a straightforward
yet effective approach that yields state-of-the-art performance on one-shot
segmentation benchmarks. Notably, SEGIC can be easily generalized to diverse
tasks, including video object segmentation and open-vocabulary segmentation.
Code will be available at https://github.com/MengLcool/SEGIC.

利用上下文分割框架（SEGIC）的元学习和端到端设计，可以实现在只有少数示例图像的情况下对新图像进行分割，并显著降低标记和训练成本，同时在一次性分割基准测试中取得最先进的性能。