Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Men, Aidong"'
Autor:
Xu, Yinsong, Wang, Yipei, Shen, Ziyi, Gayo, Iani J. M. B., Thorley, Natasha, Punwani, Shonit, Men, Aidong, Barratt, Dean, Chen, Qingchao, Hu, Yipeng
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinical practice, pathologists determine the Gleason groups based on specim
Externí odkaz:
http://arxiv.org/abs/2407.05796
Single-camera-training person re-identification (SCT re-ID) aims to train a re-ID model using SCT datasets where each person appears in only one camera. The main challenge of SCT re-ID is to learn camera-invariant feature representations without cros
Externí odkaz:
http://arxiv.org/abs/2406.14797
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless, there exists unseen
Externí odkaz:
http://arxiv.org/abs/2404.09011
Conventional Multi-Source Free Domain Adaptation (MSFDA) assumes that each source domain provides a single source model, and all source models adopt a uniform architecture. This paper introduces Zoo-MSFDA, a more general setting that allows each sour
Externí odkaz:
http://arxiv.org/abs/2403.01582
Medical image segmentation has immense clinical applicability but remains a challenge despite advancements in deep learning. The Segment Anything Model (SAM) exhibits potential in this field, yet the requirement for expertise intervention and the dom
Externí odkaz:
http://arxiv.org/abs/2311.06400
Unsupervised domain adaptation(UDA) and Source-free UDA(SFUDA) methods formulate the problem involving two domains: source and target. They typically employ a standard training approach that begins with models pre-trained on large-scale datasets e.g.
Externí odkaz:
http://arxiv.org/abs/2308.03097
Domain Generalization (DG) is essentially a sub-branch of out-of-distribution generalization, which trains models from multiple source domains and generalizes to unseen target domains. Recently, some domain generalization algorithms have emerged, but
Externí odkaz:
http://arxiv.org/abs/2301.09120
Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on
Externí odkaz:
http://arxiv.org/abs/2208.13986
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e.g., art, real, painting, quickdraw, etc. We argue that this is not realistic as it is implausible to define the real-world d
Externí odkaz:
http://arxiv.org/abs/2208.13121
Publikováno v:
ECCV 2022 Workshop
Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue.
Externí odkaz:
http://arxiv.org/abs/2208.10722