Search Results - "Modulation Module"
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1
Authors:
Source: Frontiers in Medicine, Vol 12 (2025)
Subject Terms: medical image segmentation, self-supervised learning, adaptive feature modulation module, bi-directional fusion module, multi-branch vision mamba, Medicine (General), R5-920
File Description: electronic resource
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2
Authors: Min WANG, Peidong WANG
Source: Journal of Geodesy and Geoinformation Science, Vol 6, Iss 4, Pp 40-47 (2023)
Subject Terms: remote sensing images, semantic segmentation, swin transformer, feature modulation module, Science, Geodesy, QB275-343
File Description: electronic resource
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3
Authors:
Source: CommIT Journal, Vol 18, Iss 1 (2024)
Subject Terms: Gradient Conflict Mitigation Methods, Multi-Task Learning, Project Conflicting Gradients (PCGrad), Modulation Module, Language-Specific Subnetworks (LaSS), Telecommunication, TK5101-6720, Information technology, T58.5-58.64
File Description: electronic resource
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4
Authors: et al.
Source: Archives of Electrical Engineering, Vol vol. 71, Iss No 1, Pp 21-35 (2022)
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5
Authors:
Source: CommIT (Communication and Information Technology) Journal; Vol. 18 No. 1 (2024): CommIT Journal ; 99-107 ; 2460-7010 ; 1979-2484
Subject Terms: Gradient Conflict Mitigation Methods, Multi-Task Learning, Project Conflicting Gradients (PCGrad), Modulation Module, Language-Specific Subnetworks (LaSS)
File Description: application/pdf
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6
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
7
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
8
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
9
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
10
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
11
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
12
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
13
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
14
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
15
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
16
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
17
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
18
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
19
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability -
20
Authors: et al.
Subject Terms: Biotechnology, Science Policy, Space Science, Biological Sciences not elsewhere classified, Information Systems not elsewhere classified, weighted fusion strategy, sampling blur caused, numerous consecutive convolutions, helps minimize errors, excellent generalization performance, bringing new perspectives, reduces computational cost, excessive computational costs, expanding feature maps, efficient modulation module, conventional upsampling component, existing models overlook, detect lung cancer, input ct images, computational overhead, feature extraction, dysample module, dynamic upsampling, ct images,
%22">xlink ">, two datasets, results show, recent years, insufficient precision, good scalability
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