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学者姓名：段立娟
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Abstract ：
Recently, remote sensing images have been widely used in many scenarios, gradually becoming the focus of social attention. Nevertheless, the limited annotation of scarce classes severely reduces segmentation performance. This phenomenon is more prominent in remote sensing image segmentation. Given this, we focus on image fusion and model feedback, proposing a multi-strategy method called MSAug to address the remote sensing imbalance problem. Firstly, we crop rare class images multiple times based on prior knowledge at the image patch level to provide more balanced samples. Secondly, we design an adaptive image enhancement module at the model feedback level to accurately classify rare classes at each stage and dynamically paste and mask different classes to further improve the model's recognition capabilities. The MSAug method is highly flexible and can be plug-and-play. Experimental results on remote sensing image segmentation datasets show that adding MSAug to any remote sensing image semantic segmentation network can bring varying degrees of performance improvement.
Keyword ：
Semantic segmentation Semantic segmentation Remote sensing images Remote sensing images Data augmentation Data augmentation Rare classes Rare classes
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| GB/T 7714 | Gong, Zhi , Duan, Lijuan , Xiao, Fengjin et al. MSAug: Multi-Strategy Augmentation for rare classes in semantic segmentation of remote sensing images [J]. | DISPLAYS , 2024 , 84 . |
| MLA | Gong, Zhi et al. "MSAug: Multi-Strategy Augmentation for rare classes in semantic segmentation of remote sensing images" . | DISPLAYS 84 (2024) . |
| APA | Gong, Zhi , Duan, Lijuan , Xiao, Fengjin , Wang, Yuxi . MSAug: Multi-Strategy Augmentation for rare classes in semantic segmentation of remote sensing images . | DISPLAYS , 2024 , 84 . |
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Abstract ：
Zero-shot object detection aims to identify objects from unseen categories not present during training. Existing methods rely on category labels to create pseudo-features for unseen categories, but they face limitations in exploring semantic information and lack robustness. To address these issues, we introduce a novel framework, EKZSD, enhancing zero-shot object detection by incorporating external knowledge and contrastive paradigms. This framework enriches semantic diversity, enhancing discriminative ability and robustness. Specifically, we introduce a novel external knowledge extraction module that leverages attribute and relationship prompts to enrich semantic information. Moreover, a novel external knowledge contrastive learning module is proposed to enhance the model's discriminative and robust capabilities by exploring pseudo- visual features. Additionally, we use cycle consistency learning to align generated visual features with original semantic features and adversarial learning to align visual features with semantic features. Collaboratively trained with contrast learning loss, cycle consistency loss, adversarial learning loss, and classification loss, our framework outperforms superior performance on the MSCOCO and Ship-43 datasets, as demonstrated in experimental results.
Keyword ：
External knowledge External knowledge Zero-shot object detection Zero-shot object detection Supervised contrastive learning Supervised contrastive learning
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| GB/T 7714 | Duan, Lijuan , Liu, Guangyuan , En, Qing et al. Enhancing zero-shot object detection with external knowledge-guided robust contrast learning [J]. | PATTERN RECOGNITION LETTERS , 2024 , 185 : 152-159 . |
| MLA | Duan, Lijuan et al. "Enhancing zero-shot object detection with external knowledge-guided robust contrast learning" . | PATTERN RECOGNITION LETTERS 185 (2024) : 152-159 . |
| APA | Duan, Lijuan , Liu, Guangyuan , En, Qing , Liu, Zhaoying , Gong, Zhi , Ma, Bian . Enhancing zero-shot object detection with external knowledge-guided robust contrast learning . | PATTERN RECOGNITION LETTERS , 2024 , 185 , 152-159 . |
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Abstract ：
A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link prediction difficult, especially in the setting of complex relations. At the same time, current techniques that rely on distance models encapsulate entities within Euclidean space, limiting their ability to depict nuanced relationships and failing to capture their semantic importance. This research offers a unique strategy based on Gibbs sampling and connection embedding to improve the model's competency in handling link prediction within complex relationships. Gibbs sampling is initially used to obtain high-quality negative samples. Following that, the triplet entities are mapped onto a hyperplane defined by the connection. This procedure produces complicated relationship embeddings loaded with semantic information. Through metric learning, this process produces complex relationship embeddings imbued with semantic meaning. Finally, the method's effectiveness is demonstrated on three link prediction benchmark datasets FB15k-237, WN11RR and FB15k.
Keyword ：
knowledge graph embedding knowledge graph embedding metric learning metric learning negative sampling negative sampling relation fusion relation fusion semantic extraction semantic extraction link prediction link prediction
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| GB/T 7714 | Duan, Lijuan , Han, Shengwen , Jiang, Wei et al. Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (8) . |
| MLA | Duan, Lijuan et al. "Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding" . | APPLIED SCIENCES-BASEL 14 . 8 (2024) . |
| APA | Duan, Lijuan , Han, Shengwen , Jiang, Wei , He, Meng , Qiao, Yuanhua . Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding . | APPLIED SCIENCES-BASEL , 2024 , 14 (8) . |
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Abstract ：
本发明公开了一种改善N1期类别混淆的多模态多尺度睡眠分期方法。对原始睡眠数据进行预处理，获得睡眠数据样本。针对N1期睡眠数据少的情况，使用基于叠取策略的数据增强算法生成N1期，减轻了N1期少对睡眠分期的影响。针对睡眠数据利用不充分的问题，设计了多模态多尺度特征提取模块，对不同模态的数据进行不同处理，且使用多尺度特征提取方式对EEG模态进行细粒度特征提取，提高特征的有效性，初步解决N1期难区分问题，提高N1期的分类准确率。针对N1期容易与N2期和REM期混淆的问题，使用对比学习的方法，使得同一分期睡眠数据特征相似度更高，不同分期睡眠数据特征相似度相对降低，进一步提高N1期的可区分性。本发明在睡眠分期任务中N1期准确率最高。
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| GB/T 7714 | 段立娟 , 尹悦 . 一种改善N1期类别混淆的多模态多尺度睡眠分期方法 : CN202310152184.2[P]. | 2023-02-22 . |
| MLA | 段立娟 et al. "一种改善N1期类别混淆的多模态多尺度睡眠分期方法" : CN202310152184.2. | 2023-02-22 . |
| APA | 段立娟 , 尹悦 . 一种改善N1期类别混淆的多模态多尺度睡眠分期方法 : CN202310152184.2. | 2023-02-22 . |
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Abstract ：
本发明涉及一种基于图注意力网络和稀疏编码的多通道EEG信号识别方法。首先对多通道脑电信号进行预处理，获得若干多通道脑电信号数据样本。接下来对每一个数据样本进行分频带处理，分为五种子频带，采用上述两种特征提取方式分别构造五种脑功能网络。接下来对五种脑功能网络进行融合，将其脑功能节点特征进行拼接作为融合后的脑功能节点特征；对五种脑功能连接特征取平均值，然后进行进行阈值处理去除无效连接，作为融合后的脑功能连接特征。将融合后的脑功能网络通过图注意力网络模型来还原真实的脑功能连接特征，并使用自编码器对脑功能连接稀疏特征进行降维和特征增强，进行特征降维，最终将两种特征融合并进行分类。本发明分类准确率最高。
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| GB/T 7714 | 段立娟 , 邹鑫宇 , 乔元华 . 一种基于图注意力网络和稀疏编码的静息态多通道脑电信号识别方法 : CN202310152183.8[P]. | 2023-02-22 . |
| MLA | 段立娟 et al. "一种基于图注意力网络和稀疏编码的静息态多通道脑电信号识别方法" : CN202310152183.8. | 2023-02-22 . |
| APA | 段立娟 , 邹鑫宇 , 乔元华 . 一种基于图注意力网络和稀疏编码的静息态多通道脑电信号识别方法 : CN202310152183.8. | 2023-02-22 . |
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Abstract ：
Few-shot learning aims to recognize novel categories solely relying on a few labeled samples, with existing few-shot methods primarily focusing on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured, and the actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we investigate an interesting and challenging cross-domain few-shot learning task, where the training and testing tasks employ different domains. Specifically, we propose aMeta-Memory scheme to bridge the domain gap between source and target domains, leveraging style-memory and content-memory components. The former stores intra-domain style information from source domain instances and provides a richer feature distribution. The latter stores semantic information through exploration of knowledge of different categories. Under the contrastive learning strategy, our model effectively alleviates the cross-domain problem in few-shot learning. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on cross-domain few-shot semantic segmentation tasks on the COCO-20(i), PASCAL-5(i), FSS-1000, and SUIM datasets and positively affects few-shot classification tasks on Meta-Dataset.
Keyword ：
Memory Memory few-shot learning few-shot learning cross-domain cross-domain semantic segmentation semantic segmentation
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| GB/T 7714 | Wang, Wenjian , Duan, Lijuan , Wang, Yuxi et al. MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2023 , 45 (12) : 15018-15035 . |
| MLA | Wang, Wenjian et al. "MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45 . 12 (2023) : 15018-15035 . |
| APA | Wang, Wenjian , Duan, Lijuan , Wang, Yuxi , Fan, Junsong , Zhang, Zhaoxiang . MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2023 , 45 (12) , 15018-15035 . |
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Abstract ：
基于知识蒸馏和域自适应的双教师睡眠分期特征迁移方法，属于信号处理和模式识别领域。首先对睡眠脑电和眼电信号进行预处理，获得若干多模态睡眠信号数据样本。接下来对源域和目标域数据的每一个样本包含的每个通道依次使用不同分辨率的Morlet小波变换提取时频特征，随后输入源域教师和目标域教师进行预训练。在对学生的训练优化时，引入冻结住特征提取器的两个教师进行指导，约束学生学习源域和目标域通用特征和目标域的域特定特征。实验证明本发明提出的模型充分利用了数据的特征进行特征迁移，在目标域数据量较少时也能得到良好效果，可以有效应对现有的自动化睡眠分期方法在面对新数据集时准确率下降的问题。
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| GB/T 7714 | 段立娟 , 张岩 . 基于知识蒸馏和域自适应的双教师睡眠分期特征迁移方法 : CN202310189447.7[P]. | 2023-02-22 . |
| MLA | 段立娟 et al. "基于知识蒸馏和域自适应的双教师睡眠分期特征迁移方法" : CN202310189447.7. | 2023-02-22 . |
| APA | 段立娟 , 张岩 . 基于知识蒸馏和域自适应的双教师睡眠分期特征迁移方法 : CN202310189447.7. | 2023-02-22 . |
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Abstract ：
本发明公开了一种基于记忆力机制的小样本跨域分割方法，该方法不但能够缓解模型对大量标注样本的依赖，还有效提高了模型对不同环境的适应能力。该方法首先在公开数据集上训练分割模型，此过程主要借助元度量机制来缓解模型对数据标签的依赖，并且通过读、写操作将带有域信息的风格化知识存储到记忆模块中。随后在使用模型时，将存储在记忆模块中的知识载入到新环境的待分割样本中，由此提高模型对不同环境的泛化性，最终顺利完成新场景的分割任务。本发明将训练过程中模型捕捉到的域泛化知识载入到样本稀少的新环境任务中，拉近了不同环境间的数据分布，使得深度模型能够有效的面对标注数据稀少的新环境，扩展了深度分割模型的泛化性与可用性。
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| GB/T 7714 | 段立娟 , 王文健 , 公智 et al. 基于记忆力机制的跨域小样本图像语义分割方法 : CN202210707799.2[P]. | 2022-06-20 . |
| MLA | 段立娟 et al. "基于记忆力机制的跨域小样本图像语义分割方法" : CN202210707799.2. | 2022-06-20 . |
| APA | 段立娟 , 王文健 , 公智 , 乔元华 . 基于记忆力机制的跨域小样本图像语义分割方法 : CN202210707799.2. | 2022-06-20 . |
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Abstract ：
本发明提供了一种基于显著稀疏强关联的fmri脑功能连接数据特征提取方法，该方法借鉴了空间自注意力机制思想，对fmri数据相关的显著性区域特征进行提取并对非显著性区域特征做稀疏化处理，而后结合不同显著性区域特征的强关联性解决数据特征提取过程中出现的样本维度高、冗余特征过多，以及特征关联信息利用不足等问题。为了客观评价所提出模型的有效性，在ABIDE和ADHD数据集上进行验证。实验结果表明，本文提出的特征提取方法有效提高了fmri脑功能连接数据的分类准确率。
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| GB/T 7714 | 段立娟 , 李明 , 张文博 et al. 一种基于显著稀疏强关联的fmri脑功能连接数据特征提取方法 : CN202210090736.7[P]. | 2022-01-26 . |
| MLA | 段立娟 et al. "一种基于显著稀疏强关联的fmri脑功能连接数据特征提取方法" : CN202210090736.7. | 2022-01-26 . |
| APA | 段立娟 , 李明 , 张文博 , 乔元华 . 一种基于显著稀疏强关联的fmri脑功能连接数据特征提取方法 : CN202210090736.7. | 2022-01-26 . |
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Abstract ：
本发明涉及一种基于混合图神经网络的软件源码漏洞检测方法，用于解决在软件源码处理过程中源码内部结构与语义信息丢失，漏洞检测效果差的问题，包括：将源码文件采用信息增强后的代码属性图表示，将信息增强后的代码属性图向量化后输入图卷积神经网络中得到局部特征矩阵；输入门控图神经网络中得到全局特征矩阵。将局部特征矩阵和全局特征矩阵拼接后输入分类器，最后输出检测结果。采用本方法能够有效保留源码内部的结构和语义信息，模型训练采用焦点损失函数在损失计算时赋予正负样本不同大小的权重，避免模型过度拟合样本更多的非漏洞类别，提升了模型的漏洞检测效果。
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| GB/T 7714 | 段立娟 , 徐泽鑫 , 陈军成 . 一种基于混合图神经网络的软件源码漏洞检测方法 : CN202210274334.2[P]. | 2022-03-18 . |
| MLA | 段立娟 et al. "一种基于混合图神经网络的软件源码漏洞检测方法" : CN202210274334.2. | 2022-03-18 . |
| APA | 段立娟 , 徐泽鑫 , 陈军成 . 一种基于混合图神经网络的软件源码漏洞检测方法 : CN202210274334.2. | 2022-03-18 . |
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