Incremental Semantic Segmentation Learning

增量语义分割学习

基本信息

批准号：
21J13152
负责人：
ZHANG KAIPENG
金额：
$ 0.96万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2021
资助国家：
日本
起止时间：
2021-04-28 至 2023-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21J13152/
关键词：
Semantic segmentation Neural network Few-shot learning Active learning Neural routing by memory

项目摘要

This research aims to improve semantic segmentation through two solutions, including the passive and active solutions. The passive solution provides the computer with a few new annotated data for new categories to make the computer able to segment the image regions of new categories. The active solution makes the computer able to discover valuable data during running and use them to improve the model.In 2021, we completed the passive solution by proposing a method named Segmentation by Dynamic Prototype (SDP). SDP does segmentation by searching each pixel's features nearest prototype in feature space. A prototype is a representative feature of a class. During running, it is dynamically constructed by a few new annotated data and old data. We submitted this work to a journal, and it is under review so far.As for the active solution, we proposed a continual active learning method for semantic segmentation. It can continually select informative images to annotate and feed them to the model to improve accuracies. But the improvement is not satisfactory so far, and we will do more research in the next.Besides, during the research, we found large redundant storage and RAM resources in cloud servers. Thus, we proposed a method named Neural Routing by Memory, which utilizes the redundant resources to improve accuracies. The work was accepted by NeurIPS 2021.

这项研究旨在通过包括被动和主动解决方案在内的两种解决方案来改善语义细分。被动解决方案为计算机提供了一些新类别的新注释数据，以使计算机能够分割新类别的图像区域。活动解决方案使计算机能够在运行过程中发现有价值的数据并使用它们来改进模型。在2021年，我们通过提出一种通过动态原型（SDP）提出一个名为“分割的方法”完成了被动解决方案。 SDP通过在特征空间中搜索每个像素的特征最近的原型来进行分割。原型是类的代表性特征。在运行过程中，它是通过一些新的注释数据和旧数据动态构造的。我们将这项工作提交给期刊，到目前为止，它都在审查中。对于主动解决方案，我们提出了一种持续的主动学习方法，以进行语义细分。它可以不断选择信息图像以注释并将其馈送到模型中以提高准确性。但是，到目前为止，改进并不令人满意，我们将在下一项研究中进行更多的研究。Besides，在研究期间，我们在云服务器中发现了大量的冗余存储和RAM资源。因此，我们提出了一种通过内存称为神经路由的方法，该方法利用冗余资源来提高准确性。这项工作被神经2021所接受。