AIMS: All-Inclusive Multi-Level Segmentation

• URL: http://arxiv.org/abs/2305.17768v1
• Date: Sun, 28 May 2023 16:28:49 GMT
• Title: AIMS: All-Inclusive Multi-Level Segmentation
• Authors: Lu Qi, Jason Kuen, Weidong Guo, Jiuxiang Gu, Zhe Lin, Bo Du, Yu Xu, Ming-Hsuan Yang
• Abstract summary: We propose a new task, All-Inclusive Multi-Level (AIMS), which segments visual regions into three levels: part, entity, and relation. 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.
• Score: 93.5041381700744
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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