Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for differentlevel 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 segment anything. We will make our code and training model publicly available.

Ninedifferentdebiasing algorithms (and a baseline) have been evaluated with this dataset using the popular ResNet-18 network[36]. CelebA contains faces of celebrities with several binary task labelsandtwoprotected labels(genderandyouth). Table 3showsthe prediction results from a biased binary classifier and its bias values using the seven metrics. Without losing generality, we consider "Sport" the positive class in the binary classifier. Following the DP formula in Appendix A.2, for the "Sport" class, thePPRfemale is 45.0% (90 /200), andPPRmale is65.0%

Neural Information Processing Systems http://nips.cc/