Official pytorch implementation of SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation (CVPR 2023) - lslrh/SIM
2). Regressing Mask-level Instances 该阶段的输出是对每一个box的proposal进行像素级的mask分割。 Given a box predicted by stage 1, we extract a feature of this box byRegion-of-Interest (RoI) pooling. The purpose of RoI pooling is for producing a fixed-size feature from an arbitrary box, wh...
在InstanceFCN中,引入平移可变性,采用全卷积网络来获得实例mask proposal. 采用 k2k2k^2 position-sensitive score maps,对应了 k×kk×kk×k 对物体的均分单元格. 如(b). 每个score map具有原始图片的相同空间范围,分辨率较低,比如16×. 每个score表示在相对位置中,像素点属于某个物体实例的概率. 如(b)中,...
However, conventional FCNs do not work for the instance-aware semantic segmentation task, which requires the detection and segmentation of individual object instances. The limitation is inherent. Because convolution is translation invariant, the same image pixel receives the same responses (thus classifica...
In this work, we use recent advances in conditional diffusion modeling to formulate a first-of-its-kind nuclei-aware semantic tissue generation framework (NASDM) which can synthesize realistic tissue samples given a semantic instance mask of up to six different nuclei types, enabling pixel-perfect...
InstanceFCN主要是用来生成mask proposal,也有着一些缺点,比如无法判断语义类别,需要一个后续的网络辅助判别,也就是说它不是一个end to end的结构。 建立在InstanceFCN的基础上,作者提出了他们的结构(即FCIS)。FCIS的两个子任务(目标分割和检测)不仅共享卷积特征,也共享score maps。 在实例分割,同一像素既可以是一...
FCIS provides a simple, fast and accurate framework for instance segmentation. Different fromMNC, FCIS performs instance mask estimation and categorization jointly and simultanously, and estimates class-specific masks. We did not exploit the various techniques & tricks in the Mask RCNN system, like ...
这样可以明显降低运算量。具体是通过Region Convolution 来实现的。 M can be implemented as a binary mask, where the pixels inside M equal one, otherwise zero M 是一个二值掩模图像。 最终的分割结果是将 L1, L2,L3 的结果综合起来。 VOC12 test set 和 Cityscapes test set 结果对比...
Additionally, we present a variant that we name UDA + ADA consisting of applying the region annotation of the first method to the predictions made by a UDA method, which can label the most uncertain pixels as “unknown”, for instance, by simply thresholding the highest class probability. By...
总结:在语义分割任务中,我们最为常见的还是bce损失和dice损失以及他们的变体,譬如soft dice,focal loss等等(带权重的bce),这篇文章提出了blob损失,主要用于修复语义分割中,类间实例分布不平衡的问题。这…