Current weakly supervised semantic segmentation methods usually generate noisy pseudo-labels. Training segmentation models with these labels tends to overfit the noise, leading to poor performance. Existing approaches often rely on iterative updates of pseudo-labels at pixel or image-level, ignoring the importance of region-level characteristics. The recently introduced Segment Anything Model (SAM) advances multiple approaches by fusing such region-level masks with noisy pseudo-labels. However, the fusion of noisy pseudo-labels using SAM is still challenging due to the lack of semantic information. To address these challenges, we propose a egion-based nline elective xamination (ROSE). To be specific, we first consolidate SAM masks in a bottom-up manner to form a unified region prior. Then, leveraging these priors, region-level visual information is aggregated through the proposed region voting strategy. Furthermore, a cross-view selective examination method effectively explores semantic consistency between different image views and performs an examination to correct noisy pseudo-labels. The experimental results show that our ROSE achieves a new state-of-the-art on the Pascal VOC and COCO datasets. Moreover, the training time of our ROSE is over 10 times faster than previous methods.

中文翻译：

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基于区域的弱监督语义分割在线抽查

当前的弱监督语义分割方法通常会产生噪声伪标签。使用这些标签训练分割模型往往会过度拟合噪声，从而导致性能不佳。现有方法通常依赖于像素或图像级别伪标签的迭代更新，忽略了区域级别特征的重要性。最近推出的分段任意模型 (SAM) 通过将此类区域级掩码与噪声伪标签融合，推进了多种方法。然而，由于缺乏语义信息，使用 SAM 融合噪声伪标签仍然具有挑战性。为了应对这些挑战，我们提出了一种基于区域的在线选择性检查（ROSE）。具体来说，我们首先以自下而上的方式合​​并SAM掩模以形成统一的先验区域。然后，利用这些先验，通过提出的区域投票策略聚合区域级视觉信息。此外，跨视图选择性检查方法有效地探索了不同图像视图之间的语义一致性，并进行检查以纠正噪声伪标签。实验结果表明，我们的 ROSE 在 Pascal VOC 和 COCO 数据集上达到了新的 state-of-the-art。此外，我们的 ROSE 的训练时间比以前的方法快了 10 倍以上。