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.

翻译：无监督实例分割旨在无需依赖人工标注数据的情况下，对图像中不同的物体实例进行分割。该领域近期取得了显著进展，部分得益于自监督模型（如DINO）所提供的丰富视觉特征表示所具备的强局部对应性。当前最先进的方法利用自监督特征将图像表示为图结构，并通过求解广义特征值系统（即归一化割）来生成前景掩码。尽管该方法有效，但其伴随的计算需求限制了性能，导致推理速度较慢。本文提出提示与合并方法（ProMerge），该方法利用自监督视觉特征获取图像块的初始分组，并通过对这些分割区域实施策略性合并，辅以基于复杂背景的掩码剪枝技术。与当前基于归一化割的最先进方法相比，ProMerge不仅能够获得具有竞争力的结果，还能显著减少推理时间。此外，当使用我们的掩码预测作为伪标签训练物体检测器时，所得检测器在多个具有挑战性的实例分割基准测试中超越了当前领先的无监督模型。