论文标题
长尾语义分段的区域重新平衡
Region Rebalance for Long-Tailed Semantic Segmentation
论文作者
论文摘要
在本文中，我们研究了语义分割中类不平衡的问题。我们首先调查并确定通过像素重新平衡解决此问题的主要挑战。然后，基于我们的分析得出了一个简单而有效的区域重平方案。在我们的解决方案中，将属于同一类的像素特征分组为区域特征，并且在训练过程中通过辅助区域的重新平衡分支应用重新平衡的区域分类器。为了验证我们方法的灵活性和有效性，我们将区域重新平衡模块应用于各种语义分割方法，例如DeepLabV3+，Ocrnet和Swin。我们的策略在挑战性的ADE20K和可可固定基准方面取得了一致的改进。特别是，随着拟议的区域重新平衡计划，最先进的BEIT在ADE20K Val设置的MIOU方面获得 +0.7％的增长。
In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebalance. Then a simple and yet effective region rebalance scheme is derived based on our analysis. In our solution, pixel features belonging to the same class are grouped into region features, and a rebalanced region classifier is applied via an auxiliary region rebalance branch during training. To verify the flexibility and effectiveness of our method, we apply the region rebalance module into various semantic segmentation methods, such as Deeplabv3+, OCRNet, and Swin. Our strategy achieves consistent improvement on the challenging ADE20K and COCO-Stuff benchmark. In particular, with the proposed region rebalance scheme, state-of-the-art BEiT receives +0.7% gain in terms of mIoU on the ADE20K val set.
