Interactive Segmentation (IS) segments specific objects or parts in the image
according to user input. Current IS pipelines fall into two categories:
single-granularity output and multi-granularity output. The latter aims to
alleviate the spatial ambiguity present in the former. However, the
multi-granularity output pipeline suffers from limited interaction flexibility
and produces redundant results. In this work, we introduce
Granularity-Controllable Interactive Segmentation (GraCo), a novel approach
that allows precise control of prediction granularity by introducing additional
parameters to input. This enhances the customization of the interactive system
and eliminates redundancy while resolving ambiguity. Nevertheless, the
exorbitant cost of annotating multi-granularity masks and the lack of available
datasets with granularity annotations make it difficult for models to acquire
the necessary guidance to control output granularity. To address this problem,
we design an any-granularity mask generator that exploits the semantic property
of the pre-trained IS model to automatically generate abundant mask-granularity
pairs without requiring additional manual annotation. Based on these pairs, we
propose a granularity-controllable learning strategy that efficiently imparts
the granularity controllability to the IS model. Extensive experiments on
intricate scenarios at object and part levels demonstrate that our GraCo has
significant advantages over previous methods. This highlights the potential of
GraCo to be a flexible annotation tool, capable of adapting to diverse
segmentation scenarios. The project page: this https URL

我们介绍了一种名为 Granularity-Controllable Interactive Segmentation（GraCo）的新方法，通过引入额外参数来精确控制预测粒度，增强了交互系统的自定义性，消除了冗余并解决了模糊性。我们设计了一个任意粒度掩码生成器，利用预训练的 IS 模型的语义属性自动生成丰富的掩码 - 粒度对，以提供必要的引导来控制输出粒度，这使得 GraCo 比先前的方法具有显著优势，展示了它作为灵活的标注工具在各种分割场景中的潜力。