Recently, the first foundation model developed specifically for vision tasks
was developed, termed the "Segment Anything Model" (SAM). SAM can segment
objects in input imagery based upon cheap input prompts, such as one (or more)
points, a bounding box, or a mask. The authors examined the zero-shot image
segmentation accuracy of SAM on a large number of vision benchmark tasks and
found that SAM usually achieved recognition accuracy similar to, or sometimes
exceeding, vision models that had been trained on the target tasks. The
impressive generalization of SAM for segmentation has major implications for
vision researchers working on natural imagery. In this work, we examine whether
SAM's impressive performance extends to overhead imagery problems, and help
guide the community's response to its development. We examine SAM's performance
on a set of diverse and widely-studied benchmark tasks. We find that SAM does
often generalize well to overhead imagery, although it fails in some cases due
to the unique characteristics of overhead imagery and the target objects. We
report on these unique systematic failure cases for remote sensing imagery that
may comprise useful future research for the community. Note that this is a
working paper, and it will be updated as additional analysis and results are
completed.

研究了针对视觉任务开发的第一个基础模型，称为 Segment Anything Model（SAM）。SAM 可以基于便宜的输入提示，如一个或多个点、边界框或掩码，在输入的图像中分割对象。作者在大量的视觉基准任务中研究了 SAM 的零样本图像分割准确性，并发现 SAM 通常实现了与目标任务上进行了训练的视觉模型类似或有时超过其识别精度。他们检查了 SAM 在多样化、广泛研究的基准任务集上的表现。然而，作者还研究了 SAM 在航空图像问题中的表现，他们发现虽然 SAM 在航空图像上通常表现良好，但在某些情况下由于航空图像和目标对象的独特特征而失败。