简介:本文介绍了ICCV 2019的一篇关于小样本图像语义分割的论文《PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment》。PANet通过度量学习方法,从支持集中的少量标注样本中学习类的原型表示,并通过非参数度量学习对查询图像进行分割。该方法在PASCAL-5i数据集上取得了显著的性能提升,1-shot和5-shot设置...
文章链接:PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment 代码链接:github.com/kaixin96/PAN 出发点 过去的方法没有区分知识提取和分割的过程,这可能是有问题的,因为分割模型表示和支持图像的语义特征混合在一起。 仅将支持集的注释用于掩码。 创新点 提出了PANet的新网络模型,通过反向执行小样...
论文阅读笔记《PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment》,程序员大本营,技术文章内容聚合第一站。
PANet在训练过程中进一步引入了一种原型对齐正则化,通过从查询到支持的反向执行few-shot分割,将原型从嵌入空间中支持和查询图像对齐。 模型细节 1.特征图提取:以2-way 1-shot为例,首先通过权重相同的空洞卷积的VGG-16网络作为backbone来提取特征图。 2.原型学习:随后在块(a)中,PANet执行支持查询few-shot分割。
【Few-Shot Segmentation论文阅读笔记】PANet: Few-Shot Image Semantic Segmentation with Prototype , ICCV, 2019,程序员大本营,技术文章内容聚合第一站。
Few-shot semantic segmentation (FSS) is a challenging task that aims to learn to segment novel categories with only a few labeled images, and it has a wide range of real-world applications. Recently, the performance of FSS has been greatly promoted by using deep learning approaches. In this...
Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation...
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment (2019.08 ICCV) 与检测的 PANet 无关,重名了 support 和 query image 公用 feature extractor,对 support feature 做 mask GAP 得到 prototypes。 query feature 与 prototypes 计算 cosine distance ...
Instead, it is only the query im- age IQ as testing common semantic segmentation models. To better distinguish between FS-Seg and GFS-Seg, we illustrate a 2-way K-shot task of FS-Seg and a case of GFS- Seg with the same query image in Figure 2, where Cow and Motorbike are novel ...
Few-shot 3D Point Cloud Semantic Segmentation Na Zhao Tat-Seng Chua Gim Hee Lee Department of Computer Science, National University of Singapore {nazhao, chuats, gimhee.lee}@comp.nus.edu.sg Abstract Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These ...