Transfer adaptation learning: A decade survey
The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …
environment. Domain is referred to as the state of the world at a certain moment. A research …
Semantic image segmentation: Two decades of research
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …
vision applications, providing key information for the global understanding of an image. This …
Language-driven semantic segmentation
We present LSeg, a novel model for language-driven semantic image segmentation. LSeg
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …
Tvt: Transferable vision transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learnt from a
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …
labeled source domain to an unlabeled target domain. Previous work is mainly built upon …
DecoupleNet: Decoupled network for domain adaptive semantic segmentation
Unsupervised domain adaptation in semantic segmentation alleviates the reliance on
expensive pixel-wise annotation. It uses a labeled source domain dataset as well as …
expensive pixel-wise annotation. It uses a labeled source domain dataset as well as …
Unsupervised domain adaptation for semantic image segmentation: a comprehensive survey
Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …
applications, providing key information for the global understanding of an image. Yet, the …
Empirical generalization study: Unsupervised domain adaptation vs. domain generalization methods for semantic segmentation in the wild
For autonomous vehicles and mobile robots to safely operate in the real world, ie, the wild,
scene understanding models should perform well in the many different scenarios that can be …
scene understanding models should perform well in the many different scenarios that can be …
Adaptive betweenness clustering for semi-supervised domain adaptation
Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …
aims to significantly improve the classification performance and generalization capability of …
Towards better robustness against common corruptions for unsupervised domain adaptation
Recent studies have investigated how to achieve robustness for unsupervised domain
adaptation (UDA). While most efforts focus on adversarial robustness, ie how the model …
adaptation (UDA). While most efforts focus on adversarial robustness, ie how the model …
Toward adversarial robustness in unlabeled target domains
In the past several years, various adversarial training (AT) approaches have been invented
to robustify deep learning model against adversarial attacks. However, mainstream AT …
to robustify deep learning model against adversarial attacks. However, mainstream AT …