标题： 无监督多目标分割的中心边界推理 显示英文标题

标题： unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning

摘要： 我们研究了单张图像上无监督多目标分割这一具有挑战性的问题。现有方法依赖于图像重建目标来学习目标性（objectness）或者利用预训练的图像特征来聚合相似像素，通常只能成功分割简单的合成对象或发现有限数量的真实世界对象。在本文中，我们提出了 unMORE，这是一种新颖的两阶段管道，旨在识别现实世界图像中的许多复杂对象。我们的方法的关键在于第一阶段显式地学习三个精心定义的对象中心表示层。随后，我们的多目标推理模块利用这些学到的目标先验知识在第二阶段发现多个对象。值得注意的是，这个推理模块完全不需要网络且不需要人工标签。广泛的实验表明，unMORE 在包括具有挑战性的 COCO 数据集在内的 6 个真实世界基准数据集上显著优于所有现有的无监督方法，实现了最先进的对象分割结果。令人印象深刻的是，我们的方法在所有基线崩溃的拥挤图像中表现出色。 显示英文摘要

摘要： We study the challenging problem of unsupervised multi-object segmentation on single images. Existing methods, which rely on image reconstruction objectives to learn objectness or leverage pretrained image features to group similar pixels, often succeed only in segmenting simple synthetic objects or discovering a limited number of real-world objects. In this paper, we introduce unMORE, a novel two-stage pipeline designed to identify many complex objects in real-world images. The key to our approach involves explicitly learning three levels of carefully defined object-centric representations in the first stage. Subsequently, our multi-object reasoning module utilizes these learned object priors to discover multiple objects in the second stage. Notably, this reasoning module is entirely network-free and does not require human labels. Extensive experiments demonstrate that unMORE significantly outperforms all existing unsupervised methods across 6 real-world benchmark datasets, including the challenging COCO dataset, achieving state-of-the-art object segmentation results. Remarkably, our method excels in crowded images where all baselines collapse.