【语义分割】【总结】Object-Contextual Representations for Semantic Segmentation 技术标签:语义分割计算机视觉 主要贡献 整体结构 具体方法 实验 对比实验1 粗分割的监督 对比实验2 像素-区域关系方法 Double-attention ACFNet 实验3 多尺度上下文方法 PPM ASPP 对比实验4 关系上下文
We propose to investigate the contextual representation scheme along the line of exploring the relation between a position and its context. The motivation is that the class label assigned to one pixel is the category of the object 4 that the pixel belongs to. We aim to augment the representatio...
如图2所示,使用一个线性函数(1×1卷积)来预测粗分割(软目标区域),使用像素级的交叉熵损失。所有的transform函数实现均为1×1 conv→BN→ReLU。使用一个线性函数从最终图像表示中预测最终的分割,在最终的分割预测中也应用了像素级交叉熵损失。 4.实验 4.1数据集 Cityscapes、ADE20K、LIP、PASCAL-Context、COCO-Stuff...
Segmentation Transformer: Object-Contextual Representations for Semantic SegmentationJingdong WangXiaokang ChenXilin ChenYuhui Yuan
paper:Object-Contextual Representations for Semantic Segmentation code:PyTorch Abstract OCR是MSRA和中科院的一篇语义分割工作,结合每一类的类别语义信息给每个像素加权,再和原始的pixel特征concat组成最终每个像素的特征表示,个人理解其是一个类似coarse-to-fine的语义分割过程。
1.Introduction Definition :Semantic segmentation is a problem of assigning a class label to each pixel for an image.The context of one position refers to a set of positions, e.g.,the surrounding pix…
语义分割--Learning Object Interactions and Descriptions for Semantic Image Segmentation,程序员大本营,技术文章内容聚合第一站。
OCNet: Object Context for Semantic Segmentation Int. J. Comput. Vis., 129 (2021), pp. 2375-2398, 10.1007/s11263-021-01465-9 View in ScopusGoogle Scholar Zhang et al., 2020 Zhang, Z., Sun, B., Yang, H., Huang, Q., 2020. H3dnet: 3d object detection using hybrid geometric primit...
A 'Semantic Object' in the context of Computer Science refers to an object detected in an image that is labeled as a known object based on an initial segmentation of the image into meaningful regions. AI generated definition based on: Image and Vision Computing, 2007 ...
Rich feature hierarchies for accurate object detection and semantic segmentation 一、摘要 在PASCAL VOC标准数据集上测量的目标检测性能在最近几年趋于稳定。性能最好的方法是复杂的集成系统,它通常将多个低层图像特性与高层上下文结合起来。在本文中,我们提出了一种简单、可扩展的检测算法,相对于之前VOC 2012的最佳...