AIMS: All-Inclusive Multi-Level Segmentation
Lu Qi Jason Kuen Weidong Guo Jiuxiang Gu Zhe Lin Bo Du Yu Xu Ming-Hsuan Yang
Despite the progress of image segmentation for accurate visual entity
segmentation, completing the diverse requirements of image editing applications
for different-level region-of-interest selections remains unsolved. In this
paper, we propose a new task, All-Inclusive Multi-Level Segmentation (AIMS),
which segments visual regions into three levels: part, entity, and relation
(two entities with some semantic relationships). We also build a unified AIMS
model through multi-dataset multi-task training to address the two major
challenges of annotation inconsistency and task correlation. Specifically, we
propose task complementarity, association, and prompt mask encoder for
three-level predictions. Extensive experiments demonstrate the effectiveness
and generalization capacity of our method compared to other state-of-the-art
methods on a single dataset or the concurrent work on segmenting anything. We
will make our code and training model publicly available.