Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

Class-incremental semantic segmentation aims to incrementally learn new classes while
maintaining the capability to segment old ones, and suffers catastrophic forgetting since the …

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift
in real-world Semantic Segmentation (SS) without compromising the private nature of the …

Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …

Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic
forgetting to improve discrimination. Previous work mainly exploit regularization (eg …

Continual semantic segmentation (CSS) aims to extend an existing model to tackle unseen
tasks while retaining its old knowledge. Naively fine-tuning the old model on new data leads …

This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that
improves the computational efficiency of semantic segmentation networks that use Vision …

Deep learning models are known to suffer from the problem of catastrophic forgetting when
they incrementally learn new classes. Continual learning for semantic segmentation (CSS) …

This paper investigates the capability of plain Vision Transformers (ViTs) for semantic
segmentation using the encoder–decoder framework and introduce SegViTv2. In this study …

Recent years have witnessed a great development of Convolutional Neural Networks in
semantic segmentation, where all classes of training images are simultaneously available …