Accurately determining salient regions of an image is challenging when
labeled data is scarce. DINO-based self-supervised approaches have recently
leveraged meaningful image semantics captured by patch-wise features for
locating foreground objects. Recent methods have also incorporated intuitive
priors and demonstrated value in unsupervised methods for object partitioning.
In this paper, we propose SEMPART, which jointly infers coarse and fine
bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART
preserves fine boundary details using graph-driven regularization and
successfully distills the coarse mask semantics into the fine mask. Our salient
object detection and single object localization findings suggest that SEMPART
produces high-quality masks rapidly without additional post-processing and
benefits from co-optimizing the coarse and fine branches.

该论文提出了 SEMPART 算法，它能够快速生成高质量的掩码，同时在图像的 DINO-based 语义图上联合推断粗细二分图，用图驱动的正则化方法保留了边界细节，并成功地将粗略的掩码语义提炼为精细的掩码语义。