Context-aware mixup for domain adaptive semantic segmentation
Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source
domain to an unlabeled target domain. Existing UDA-based semantic segmentation …
domain to an unlabeled target domain. Existing UDA-based semantic segmentation …
VDM-DA: Virtual domain modeling for source data-free domain adaptation
Domain adaptation aims to leverage a label-rich domain (the source domain) to help model
learning in a label-scarce domain (the target domain). Most domain adaptation methods …
learning in a label-scarce domain (the target domain). Most domain adaptation methods …
Exploring implicit domain-invariant features for domain adaptive object detection
Recent researches have made a great progress in domain adaptive object detectors. These
detectors aim to learn explicit domain-invariant features by adversarially mitigating domain …
detectors aim to learn explicit domain-invariant features by adversarially mitigating domain …
Adaptive mutual learning for unsupervised domain adaptation
Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to
unlabeled target domain. The semi-supervised method based on mean-teacher framework …
unlabeled target domain. The semi-supervised method based on mean-teacher framework …
Partial alignment for object detection in the wild
Conventional object detectors often encounter remarkable performance drops due to the
domain shift caused by environmental changes. However, labeling sufficient training data …
domain shift caused by environmental changes. However, labeling sufficient training data …
WDAN: A weighted discriminative adversarial network with dual classifiers for fine-grained open-set domain adaptation
Deep neural networks usually depend on substantial labeled data and suffer from poor
generalization to new domains. Domain adaptation can be used to resolve these issues …
generalization to new domains. Domain adaptation can be used to resolve these issues …
Rethinking domain generalization: Discriminability and generalizability
Domain generalization (DG) endeavours to develop robust models that possess strong
generalizability while preserving excellent discriminability. Nonetheless, pivotal DG …
generalizability while preserving excellent discriminability. Nonetheless, pivotal DG …
Classification certainty maximization for unsupervised domain adaptation
The bi-classifier paradigm is a common practice in unsupervised domain adaptation (UDA),
where two classifiers are leveraged to guide the model to learn domain invariant features …
where two classifiers are leveraged to guide the model to learn domain invariant features …
Instance-Dictionary Learning for Open-World Object Detection in Autonomous Driving Scenarios
This paper addresses an important and valuable open-world object detection (OWOD) in
autonomous driving scenarios, which aims to detect objects under both domain-agnostic …
autonomous driving scenarios, which aims to detect objects under both domain-agnostic …
Collaborative screening of covid-19-like disease from multi-institutional radiographs: A federated learning approach
M Abdel-Basset, H Hawash, M Abouhawwash - Mathematics, 2022 - mdpi.com
COVID-19-like pandemics are a major threat to the global health system have the potential
to cause high mortality across age groups. The advance of the Internet of Medical Things …
to cause high mortality across age groups. The advance of the Internet of Medical Things …