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学者姓名：段立娟
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摘要 ：
Few-shot semantic segmentation intends to predict pixel-level categories using only a few labeled samples. Existing few-shot methods focus primarily on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured. The actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains. Specifically, we first propose a meta-memory bank to improve the generalization of the segmentation network by bridging the domain gap between source and target domains. The meta-memory stores the intra-domain style information from source domain instances and transfers it to target samples. Subsequently, we adopt a new contrastive learning strategy to explore the knowledge of different categories during the training stage. The negative and positive pairs are obtained from the proposed memory-based style augmentation. Comprehensive experiments demonstrate that our proposed method achieves promising results on cross-domain few-shot semantic segmentation tasks on C000-20(i) , PASCAL-5(i), FSS-1000, and SHIM datasets.
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| GB/T 7714 | Wang, Wenjian , Duan, Lijuan , Wang, Yuxi et al. Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer [J]. | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) , 2022 : 7055-7064 . |
| MLA | Wang, Wenjian et al. "Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer" . | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022) : 7055-7064 . |
| APA | Wang, Wenjian , Duan, Lijuan , Wang, Yuxi , En, Qing , Fan, Junsong , Zhang, Zhaoxiang . Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer . | CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) , 2022 , 7055-7064 . |
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摘要 ：
近年来,全卷积神经网络有效提升了语义分割任务的准确率.然而,由于室内环境的复杂性,室内场景语义分割仍然是一个具有挑战性的问题.随着深度传感器的出现,人们开始考虑利用深度信息提升语义分割效果.以往的研究大多简单地使用等权值的拼接或求和操作来融合RGB特征和深度特征,未能充分利用RGB特征与深度特征之间的互补信息.本文提出一种基于注意力感知和语义感知的网络模型ASNet(Attention-aware and Semantic-aware Network).通过引入注意力感知多模态融合模块和语义感知多模态融合模块,有效地融合多层次的RGB特征和深度特征.其中,在注意力感知多模态融合模块中,本文设计...
关键词 ：
深度学习 深度学习 RGB-D语义分割 RGB-D语义分割 卷积神经网络 卷积神经网络 注意力模型 注意力模型 多模态融合 多模态融合
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| GB/T 7714 | 段立娟 , 孙启超 , 乔元华 et al. 基于注意力感知和语义感知的RGB-D室内图像语义分割算法 [J]. | 计算机学报 , 2021 , 44 (02) : 275-291 . |
| MLA | 段立娟 et al. "基于注意力感知和语义感知的RGB-D室内图像语义分割算法" . | 计算机学报 44 . 02 (2021) : 275-291 . |
| APA | 段立娟 , 孙启超 , 乔元华 , 陈军成 , 崔国勤 . 基于注意力感知和语义感知的RGB-D室内图像语义分割算法 . | 计算机学报 , 2021 , 44 (02) , 275-291 . |
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摘要 ：
The dynamical behaviors of a Leslie type predator-prey system are explored when the functional response is increasing for both predator and prey. Qualitative and quantitative analysis methods based on stability theory, bifurcation theory and numerical simulation are adopted. It is showed that the system is dissipative and permanent, and its solutions are bounded. Global stability of the unique positive equilibrium is investigated by constructing Dulac function and applying Poincare-Bendixson theorem. The bifurcation behaviors are further explored and the number of limit cycles is determined. By calculating the first Lyapunov number and the first two focus values, it is proved that the positive equilibrium is not a center but a weak focus of multiplicity at most two, so the system undergoes Hopf bifurcation and Bautin bifurcation. The normal form of Bautin bifurcation is also obtained by introducing the complex system. Moreover, numerical simulations are run to demonstrate the validity of theoretical results.
关键词 ：
Bautin bifurcation Bautin bifurcation Global stability Global stability Hopf bifurcation Hopf bifurcation Limit cycle Limit cycle Predator-prey system Predator-prey system
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| GB/T 7714 | Shang, Zuchong , Qiao, Yuanhua , Duan, Lijuan et al. Bifurcation analysis and global dynamics in a predator-prey system of Leslie type with an increasing functional response [J]. | ECOLOGICAL MODELLING , 2021 , 455 . |
| MLA | Shang, Zuchong et al. "Bifurcation analysis and global dynamics in a predator-prey system of Leslie type with an increasing functional response" . | ECOLOGICAL MODELLING 455 (2021) . |
| APA | Shang, Zuchong , Qiao, Yuanhua , Duan, Lijuan , Miao, Jun . Bifurcation analysis and global dynamics in a predator-prey system of Leslie type with an increasing functional response . | ECOLOGICAL MODELLING , 2021 , 455 . |
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摘要 ：
基于注意力感知和语义感知的RGB-D室内图像语义分割算法
关键词 ：
RGB-D语义分割 RGB-D语义分割 卷积神经网络 卷积神经网络 多模态融合 多模态融合 注意力模型 注意力模型 深度学习 深度学习
引用：
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| GB/T 7714 | 段立娟 , 孙启超 , 乔元华 et al. 基于注意力感知和语义感知的RGB-D室内图像语义分割算法 [J]. | 段立娟 , 2021 , 44 (2) : 275-291 . |
| MLA | 段立娟 et al. "基于注意力感知和语义感知的RGB-D室内图像语义分割算法" . | 段立娟 44 . 2 (2021) : 275-291 . |
| APA | 段立娟 , 孙启超 , 乔元华 , 陈军成 , 崔国勤 , 计算机学报 . 基于注意力感知和语义感知的RGB-D室内图像语义分割算法 . | 段立娟 , 2021 , 44 (2) , 275-291 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient's sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.
关键词 ：
sleep stage classification sleep stage classification deep learning deep learning HHT HHT multimodal physiological signals multimodal physiological signals fusion networks fusion networks
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| GB/T 7714 | Duan, Lijuan , Li, Mengying , Wang, Changming et al. A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion [J]. | FRONTIERS IN HUMAN NEUROSCIENCE , 2021 , 15 . |
| MLA | Duan, Lijuan et al. "A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion" . | FRONTIERS IN HUMAN NEUROSCIENCE 15 (2021) . |
| APA | Duan, Lijuan , Li, Mengying , Wang, Changming , Qiao, Yuanhua , Wang, Zeyu , Sha, Sha et al. A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion . | FRONTIERS IN HUMAN NEUROSCIENCE , 2021 , 15 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Nuclei segmentation plays an important role in cancer diagnosis. Automated methods for digital pathology become popular due to the developments of deep learning and neural networks. However, this task still faces challenges. Most of current techniques cannot be applied directly because of the clustered state and the large number of nuclei in images. Moreover, anchor-based methods for object detection lead a huge amount of calculation, which is even worse on pathological images with a large target density. To address these issues, we propose a novel network with an anchor-free detection and a U-shaped segmentation. An altered feature enhancement module is attached to improve the performance in dense target detection. Meanwhile, the U-Shaped structure in segmentation block ensures the aggregation of features in different dimensions generated from the backbone network. We evaluate our work on a Multi-Organ Nuclei Segmentation dataset from MICCAI 2018 challenge. In comparisons with others, our proposed method achieves state-of-the-art performance. © 2021 ACM.
关键词 ：
Chemical detection Chemical detection Deep learning Deep learning Deep neural networks Deep neural networks Object detection Object detection Object recognition Object recognition
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| GB/T 7714 | Feng, Xuan , Duan, Lijuan , Chen, Jie . An automated method with anchor-free detection and U-shaped segmentation for nuclei instance segmentation [C] . 2021 . |
| MLA | Feng, Xuan et al. "An automated method with anchor-free detection and U-shaped segmentation for nuclei instance segmentation" . (2021) . |
| APA | Feng, Xuan , Duan, Lijuan , Chen, Jie . An automated method with anchor-free detection and U-shaped segmentation for nuclei instance segmentation . (2021) . |
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摘要 ：
In this paper, a Gause type predator-prey system with constant-yield prey harvesting and monotone ascending functional response is proposed and investigated. We focus on the influence of the harvesting rate on the predator-prey system. First, equilibria corresponding to different situations are investigated, as well as the stability analysis. Then bifurcations are explored at nonhyperbolic equilibria, and we give the conditions for the occurrence of two saddle-node bifurcations by analyzing the emergence, coincidence and annihilation of equilibria. We calculate the Lyapunov number and focal values to determine the stability and the quantity of limit cycles generated by supercritical, subcritical and degenerate Hopf bifurcations. Furthermore, the system is unfolded to explore the repelling and attracting Bogdanov-Takens bifurcations by perturbing two bifurcation parameters near the cusp. It is shown that there exists one limit cycle, or one homoclinic loop, or two limit cycles for different parameter values. Therefore, the system is susceptible to both the constant-yield prey harvesting and initial values of the species. Finally, we run numerical simulations to verify the theoretical analysis. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
关键词 ：
Degenerate Hopf bifurcation Degenerate Hopf bifurcation Harvesting Harvesting Bogdanov-Takens bifurcation Bogdanov-Takens bifurcation Predator-prey system Predator-prey system Limit cycle Limit cycle
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| GB/T 7714 | Shang, Zuchong , Qiao, Yuanhua , Duan, Lijuan et al. Bifurcation analysis in a predator-prey system with an increasing functional response and constant-yield prey harvesting [J]. | MATHEMATICS AND COMPUTERS IN SIMULATION , 2021 , 190 : 976-1002 . |
| MLA | Shang, Zuchong et al. "Bifurcation analysis in a predator-prey system with an increasing functional response and constant-yield prey harvesting" . | MATHEMATICS AND COMPUTERS IN SIMULATION 190 (2021) : 976-1002 . |
| APA | Shang, Zuchong , Qiao, Yuanhua , Duan, Lijuan , Miao, Jun . Bifurcation analysis in a predator-prey system with an increasing functional response and constant-yield prey harvesting . | MATHEMATICS AND COMPUTERS IN SIMULATION , 2021 , 190 , 976-1002 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Weakly supervised video object segmentation (WSVOS) is a vital yet challenging task in which the aim is to segment pixel-level masks with only category labels. Existing methods still have certain limitations, e.g., difficulty in comprehending appropriate spatiotemporal knowledge and an inability to explore common semantic information with category labels. To overcome these challenges, we formulate a novel framework by integrating multisource saliency and incorporating an exemplar mechanism for WSVOS. Specifically, we propose a multisource saliency module to comprehend spatiotemporal knowledge by integrating spatial and temporal saliency as bottom-up cues, which can effectively eliminate disruptions due to confusing regions and identify attractive regions. Moreover, to our knowledge, we make the first attempt to incorporate an exemplar mechanism into WSVOS by proposing an adaptive exemplar module to process top-down cues, which can provide reliable guidance for co-occurring objects in intraclass videos and identify attentive regions. Our framework, which comprises the two aforementioned modules, offers a new perspective on directly constructing the correspondence between bottom-up cues and top-down cues when ground-truth information for the reference frames is lacking. Comprehensive experiments demonstrate that the proposed framework achieves state-of-the-art performance.
关键词 ：
Task analysis Task analysis video object segmentation video object segmentation Motion segmentation Motion segmentation Object segmentation Object segmentation Annotations Annotations spatiotemporal saliency spatiotemporal saliency Feature extraction Feature extraction exemplar mechanism exemplar mechanism Spatiotemporal phenomena Spatiotemporal phenomena Weakly supervised learning Weakly supervised learning Training Training
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| GB/T 7714 | En, Qing , Duan, Lijuan , Zhang, Zhaoxiang . Joint Multisource Saliency and Exemplar Mechanism for Weakly Supervised Video Object Segmentation [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 : 8155-8169 . |
| MLA | En, Qing et al. "Joint Multisource Saliency and Exemplar Mechanism for Weakly Supervised Video Object Segmentation" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 30 (2021) : 8155-8169 . |
| APA | En, Qing , Duan, Lijuan , Zhang, Zhaoxiang . Joint Multisource Saliency and Exemplar Mechanism for Weakly Supervised Video Object Segmentation . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 , 8155-8169 . |
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摘要 ：
The zero-shot semantic segmentation requires models with a strong image understanding ability. The majority of current solutions are based on direct mapping or generation. These schemes are effective in dealing with the zero-shot recognition, but they cannot fully transfer the visual dependence between objects in more complex scenarios of semantic segmentation. More importantly, the predicted results become seriously biased to the seen-category in the training set, which makes it difficult to accurately recognize the unseen-category. In view of the above two problems, we propose a novel zero-shot semantic segmentation model based on meta-learning. It is observed that the pure semantic space expression has certain limitations for the zero-shot learning. Therefore, based on the original semantic migration, we first migrate the shared information in the visual space by adding a context-module, and then migrate it in the visual and semantic dual space. At the same time, in order to solve the problem of biasness, we improve the adaptability of the model parameters by adjusting the parameters of the dual-space through the meta-learning, so that it can successfully complete the segmentation even in the face of new categories without reference samples. Experiments show that our algorithm outperforms the existing best methods in the zero-shot segmentation on three datasets of Pascal-VOC 2012, Pascal-Context and Coco-stuff. (c) 2021 Published by Elsevier B.V.
关键词 ：
Semantic-segmentation Semantic-segmentation Zero-shot learning Zero-shot learning Context Context Meta-learning Meta-learning
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| GB/T 7714 | Wang, Wenjian , Duan, Lijuan , En, Qing et al. Context-sensitive zero-shot semantic segmentation model based on meta-learning [J]. | NEUROCOMPUTING , 2021 , 465 : 465-475 . |
| MLA | Wang, Wenjian et al. "Context-sensitive zero-shot semantic segmentation model based on meta-learning" . | NEUROCOMPUTING 465 (2021) : 465-475 . |
| APA | Wang, Wenjian , Duan, Lijuan , En, Qing , Zhang, Baochang . Context-sensitive zero-shot semantic segmentation model based on meta-learning . | NEUROCOMPUTING , 2021 , 465 , 465-475 . |
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摘要 ：
The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset. © 2013 IEEE.
关键词 ：
Biomedical signal processing Biomedical signal processing Bionics Bionics Brain computer interface Brain computer interface Data mining Data mining Electroencephalography Electroencephalography Human computer interaction Human computer interaction Machine learning Machine learning
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| GB/T 7714 | Lian, Zhaoyang , Duan, Lijuan , Qiao, Yuanhua et al. The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface [J]. | IEEE Access , 2021 , 9 : 67405-67416 . |
| MLA | Lian, Zhaoyang et al. "The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface" . | IEEE Access 9 (2021) : 67405-67416 . |
| APA | Lian, Zhaoyang , Duan, Lijuan , Qiao, Yuanhua , Chen, Juncheng , Miao, Jun , Li, Mingai . The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface . | IEEE Access , 2021 , 9 , 67405-67416 . |
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