Apr 10, 2023 · Abstract:Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients.
Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models ...
This is the official implementation code of our paper "Federated Incremental Semantic Segmentation" accepted by CVPR-2023.
We propose a Forgetting-Balanced Learning (FBL) model to address heterogeneous forgetting on old classes from both intra-client and interclient aspects.
Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models ...
Federated learning (FL) has achieved rapid development in semantic segmentation, by training on multiple decentralized clients to alleviate data island that ...
Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models ...
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In addition, our framework aims to benchmark various settings, so it can apply new federated settings such as federated class-incremental learning [16], [17] .
This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions.
This is the implementation code of the CVPR 2022 paper "Federated Class-Incremental Learning". You can also find the arXiv version with supplementary materials ...