Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In this work, we reveal that informative interactions can be made by simulation with semantic-consistent yet diverse region exploration in an unsupervised paradigm. Concretely, we introduce a Multi-granularity Interaction Simulation (MIS) approach to open up a promising direction for unsupervised interactive segmentation. Drawing on the high-quality dense features produced by recent self-supervised models, we propose to gradually merge patches or regions with similar features to form more extensive regions and thus, every merged region serves as a semantic-meaningful multi-granularity proposal. By randomly sampling these proposals and simulating possible interactions based on them, we provide meaningful interaction at multiple granularities to teach the model to understand interactions. Our MIS significantly outperforms non-deep learning unsupervised methods and is even comparable with some previous deep-supervised methods without any annotation.

翻译：交互式分割允许用户通过提供物体线索按需进行分割，为图像编辑、医学图像分析等领域引入了人机交互。传统方法通常依赖海量且标注区域密集的像素级标注，通过目标导向交互与人工标注的物体掩码训练深度模型。本研究揭示，在无监督范式下，通过语义一致且多样化的区域探索进行交互模拟，即可实现信息丰富的交互。具体而言，我们提出多粒度交互模拟（MIS）方法，为无监督交互式分割开辟了颇具前景的新方向。基于近期自监督模型生成的高质量密集特征，我们逐步合并特征相似的图像块或区域以形成更大范围区域，每个合并区域均可作为具有语义含义的多粒度候选区域。通过随机采样这些候选区域并基于它们模拟可能的交互，我们可在多粒度层面提供有意义的交互，从而训练模型理解交互行为。实验表明，MIS方法显著优于非深度学习的无监督方法，甚至与某些无需任何标注的深度监督方法性能相当。