Unsupervised instance segmentation aims to segment distinct object instances in an image without relying on human-labeled data. This field has recently seen significant advancements, partly due to the strong local correspondences afforded by rich visual feature representations from self-supervised models (e.g., DINO). Recent state-of-the-art approaches use self-supervised features to represent images as graphs and solve a generalized eigenvalue system (i.e., normalized-cut) to generate foreground masks. While effective, this strategy is limited by its attendant computational demands, leading to slow inference speeds. In this paper, we propose Prompt and Merge (ProMerge), which leverages self-supervised visual features to obtain initial groupings of patches and applies a strategic merging to these segments, aided by a sophisticated background-based mask pruning technique. ProMerge not only yields competitive results but also offers a significant reduction in inference time compared to state-of-the-art normalized-cut-based approaches. Furthermore, when training an object detector using our mask predictions as pseudo-labels, the resulting detector surpasses the current leading unsupervised model on various challenging instance segmentation benchmarks.

本研究解决了无监督实例分割中基于计算效率的挑战，采用Prompt and Merge（ProMerge）方法，通过自监督视觉特征获取最初的补丁分组，并采用背景掩码修剪技术进行智能合并。研究表明，ProMerge在推断速度上显著优于传统的规范切割方法，并在使用其掩码预测作为伪标签训练目标检测器时，表现超过了当前领先的无监督模型。

ProMerge：用于无监督实例分割的提示与合并