Introduction or background of this discussion:
OSPP project: "Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs"
Contents of this discussion:
- Using the specified data set to reproduce the lifelong learning semantic segmentation algorithm on KubeEdge-Ianvs; The data set includes cityscapes, SYNTHIA, and KubeEdge SIG AI open-source cloud robot data set.
- Displaying the algorithm test report (including ranking, time, algorithm name, data set name and distribution type, test indicators, etc.) on KubeEdge-Ianvs;
Project Description:
Driving is a skill that humans do not forget in natural situations. They can easily drive in multiple geographic locations. This suggests that humans are inherently endowed with a lifelong learning capacity,
with little forgetting of previously learned visual patterns when faced with domain shifts or new objects to be recognized. In the same way, many researchers hope that the semantic segmentation model also has
the ability to use a common joint model to learn data sets of multiple scenes in sequence. It is expected that the model can gradually learn new scene domains while maintaining the performance of the old
domain, and Datasets from the old domain are not accessed.
This project aims to reproduce the WACV2022 paper Multi-Domain Incremental Learning for Semantic Segmentation lifelong learning semantic segmentation algorithm, acting on the dataset of robot vision scenes, and serving as the baseline algorithm of KubeEdge-Ianvs for developers to use. The data set includes cityscapes, SYNTHIA, and KubeEdge SIG AI open-source cloud robot data set.
Introduction or background of this discussion:
OSPP project: "Implementation of a Class Incremental Learning Algorithm Evaluation System based on Ianvs"
Contents of this discussion:
Project Description:
Driving is a skill that humans do not forget in natural situations. They can easily drive in multiple geographic locations. This suggests that humans are inherently endowed with a lifelong learning capacity,
with little forgetting of previously learned visual patterns when faced with domain shifts or new objects to be recognized. In the same way, many researchers hope that the semantic segmentation model also has
the ability to use a common joint model to learn data sets of multiple scenes in sequence. It is expected that the model can gradually learn new scene domains while maintaining the performance of the old
domain, and Datasets from the old domain are not accessed.
This project aims to reproduce the WACV2022 paper Multi-Domain Incremental Learning for Semantic Segmentation lifelong learning semantic segmentation algorithm, acting on the dataset of robot vision scenes, and serving as the baseline algorithm of KubeEdge-Ianvs for developers to use. The data set includes cityscapes, SYNTHIA, and KubeEdge SIG AI open-source cloud robot data set.