原论文：Progressive Semantic Segmentation
当对大型图片进行语义分割时，可能会导致显存炸掉。收到内存限制，可以选择下采样，或将图像划分为局部块。但前者会丢失细节，后者会却反全局视图。
条件随机场(CRF),引导滤波器（GF），两个速度慢，改进是渐进的。
深度学习的引导过滤器(DGF)可以提高推理速度
论文名称：Cross-Dataset Collaborative Learning for Semantic Segmentation
作者：Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan
期刊：CVPR2021
DAB：Dataset-Aware Block(数据集感知块)
作为网络的基本计算单元，有助于捕获o不同功能数据集之间的同质表示和异构统计。
主要由，一个数据集不变的卷积层，多个数据集特定的BatchNormal和一个激活层构成。
DAT：Dataset Alternation Training(数据集交替训练机制)
分割结果：
“我们希望预测分割图的边界区域更加准确，我们就不应该使用均匀采样，而应该更加倾向于图像边界区域。”
这是一篇用于改善图像分割问题中边缘分割效果的方法的论文的阅读笔记。该方法“将分割问题看作渲染问题”，达到了较好的效果。论文原文：PointRend: Image Segmentation as Rendering。在阅读这篇笔记之前，请确保先了解图像分割技术。对分割的技术进行简要的了解，可以参考另一篇笔记。
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at this https URL.
论文名称：RefineMask: Towards High-Quality Instance Segmentationwith Fine-Grained Features
作者：Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu
期刊：CVPR2021
The two-stage methods for instance segmentation, e.g.Mask R-CNN, have achieved excellent performance re-cently. However, the segmented masks are still very coarsedue to the downsampling operations in both the featurepyramid and the instance-wise pooling process, especiallyfor large objects. In this work, we propose a new methodcalled RefineMask for high-quality instance segmentationof objects and scenes, which incorporates fine-grained fea-tures during the instance-wise segmenting process in amulti-stage manner. Through fusing more detailed informa-tion stage by stage, RefineMask is able to refine high-qualitymasks consistently. RefineMask succeeds in segmentinghard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurateboundaries. Without bells and whistles, RefineMask yieldssignificant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN onCOCO, LVIS, and Cityscapes benchmarks respectively at asmall amount of additional computational cost. Further-more, our single-model result outperforms the winner of theLVIS Challenge 2020 by 1.3 points on the LVIS test-dev setand establishes a new state-of-the-art.
即使如Mask R-CNN这样二阶段的实例分割网路已经有了优秀的表现，但因为在特征金字塔和实例池化过程中使用了下采样操作，使得分割掩码仍然非常粗糙，尤其是对于大型物体。
在本文中，提出了RefineMask方法，用于对象和场景的高质量实例分割，它在实分割的过程中以多阶段的方式结合了细粒度特征。通过逐步融合更细节的信息，RefineMask能够始终如一地提炼出高质量的mask。
论文名称：Polarized Self-Attention: Towards High-quality Pixel-wise Regression
作者：Huajun Liu, Fuqiang Liu, Xinyi Fan
这篇笔记的写作者是AsTheStarsFall。
细粒度的像素级任务（比如语义分割）一直都是计算机视觉中非常重要的任务。不同于分类或者检测，细粒度的像素级任务要求模型在低计算开销下，能够建模高分辨率输入/输出特征的长距离依赖关系，进而来估计高度非线性的像素语义。中的注意力机制能够捕获长距离的依赖关系，但是这种方式十分复杂且对噪声敏感。
本文提出了即插即用的极化自注意力模块，该模块包含两个关键设计，以保证高质量的像素回归：