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LABEL: 作品数：487被引量：299H指数：7; 导出分析报告; 相关领域：自动化与计算机技术更多>>; 相关作者：王建华王妍李勤凡胡志辉薛戈丽更多>>; 相关机构：北京华大九天软件有限公司武汉大学东南大学首都医科大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金国家高技术研究发展计划中国博士后科学基金北京市自然科学基金更多>>

Soft-GNN:towards robust graph neural networks via self-adaptive data utilization: 《Frontiers of Computer Science》2025年第4期1-12,共12页Yao WU Hong HUANG Yu SONG Hai JIN; supported by the National Natural Science Foundation of China(Grant No.62127808).; Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high performance.However,a major concern is their robustness,particularly when faced with g...; 关键词：graph neural networks node classification label noise robustness

Robust domain adaptation with noisy and shifted label distribution: 《Frontiers of Computer Science》2025年第3期25-36,共12页Shao-Yuan LI Shi-Ji ZHAO Zheng-Tao CAO Sheng-Jun HUANG Songcan CHEN; supported by the National Key R&D Program of China(2022ZD0114801);the National Natural Science Foundation of China(Grant No.61906089);the Jiangsu Province Basic Research Program(BK20190408).; Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA me...; 关键词：unsupervised domain adaptation label noise label distribution shift SELF-TRAINING class rebalancing

Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification: 《Computers, Materials & Continua》2025年第3期3921-3941,共21页Jiyang Xu Qi Wang Xin Xiong Weidong Min Jiang Luo Di Gai Qing Han; supported by the National Natural Science Foundation of China under Grant Nos.62461037,62076117 and 62166026;the Jiangxi Provincial Natural Science Foundation under Grant Nos.20224BAB212011,20232BAB202051,20232BAB212008 and 20242BAB25078;the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.; The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information.Due to the higher similarity in appearance between vehicles compare...; 关键词：Unsupervised vehicle re-identification dual contrastive learning pseudo label refinement knowledge distillation

Distributed multi-target tracking with labeled multi-Bernoulli filter considering efficient label matching: 《Frontiers of Information Technology & Electronic Engineering》2025年第3期400-414,共15页Changwen DING Chuntao SHAO Siteng ZHOUI Di ZHOU Runle DU Jiaqi LIU; We propose a distributed labeled multi-Bernoulli(LMB)filter based on an efficient label matching method.Conventional distributed LMB filter fusion has the premise that the labels among local densities have already bee...; 关键词：Distributed multi-sensor multi-target tracking Labeled multi-Bernoulli filter Arithmetic average fusion Label matching

Multi-Scale Feature Fusion and Advanced Representation Learning for Multi Label Image Classification: 《Computers, Materials & Continua》2025年第3期5285-5306,共22页Naikang Zhong Xiao Lin Wen Du Jin Shi; supported by the National Natural Science Foundation of China(62302167,62477013);Natural Science Foundation of Shanghai(No.24ZR1456100);Science and Technology Commission of Shanghai Municipality(No.24DZ2305900);the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).; Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...; 关键词：Image classification MULTI-LABEL multi scale attention mechanisms feature fusion

Partial multi-label learning via label-specific feature corrections: 《Science China(Information Sciences)》2025年第3期91-105,共15页Jun-Yi HANG Min-Ling ZHANG; supported by National Natural Science Foundation of China(Grant No.62225602);Big Data Computing Center of Southeast University。; Partial multi-label learning(PML)allows learning from rich-semantic objects with inaccurate annotations,where a set of candidate labels are assigned to each training example but only some of them are valid.Existing ap...; 关键词：machine learning multi-label learning partial multi-label learning label-specific features feature correction

TDNN:A novel transfer discriminant neural network for gear fault diagnosis of ammunition loading system manipulator: 《Defence Technology(防务技术)》2025年第3期84-98,共15页Ming Li Longmiao Chen Manyi Wang Liuxuan Wei Yilin Jiang Tianming Chen; The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...; 关键词：Manipulator gear fault diagnosis Reciprocating machine Domain adaptation Pseudo-label training strategy Transfer discriminant neural network

Improving Multi-task GNNs for Molecular Property Prediction via Missing Label Imputation: 《Machine Intelligence Research》2025年第1期131-144,共14页Fenyu Hu Dingshuo Chen Qiang Liu Shu Wu; supported by the National Natural Science Foundation of China(Nos.62141608 and U19B 2038),the CAAI Huawei MindSpore Open Fund.; The prediction of molecular properties is a fundamental task in the field of drug discovery.Recently,graph neural networks(GNNs)have been gaining prominence in this area.Since a molecule tends to have multiple correla...; 关键词：Graph classification imbalance learning prediction bias mixture of experts multiview representations

Neuropsychological Guided Blind Image Quality Assessment via Noisy Label Optimization: 《China Communications》2025年第2期173-187,共15页Zhu Jinchi Ma Xiaoyu Liu Chang Yu Dingguo; supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200);the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).; Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of th...; 关键词：blind image quality assessment deep neural network ELECTROENCEPHALOGRAM persistent homology

KD-Crowd:a knowledge distillation framework for learning from crowds: 《Frontiers of Computer Science》2025年第1期119-130,共12页Shaoyuan LI Yuxiang ZHENG Ye SHI Shengjun HUANG Songcan CHEN; supported by the National Key R&D Program of China(2022ZD0114801);the National Natural Science Foundation of China(Grant No.61906089);the Jiangsu Province Basic Research Program(BK20190408).; Recently, crowdsourcing has established itself as an efficient labeling solution by distributing tasks to crowd workers. As the workers can make mistakes with diverse expertise, one core learning task is to estimate e...; 关键词：crowdsourcing label noise worker expertise knowledge distillation robust learning

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