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学者姓名：段立娟
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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 ：
Multidirectional associative memory neural networks (MAMNNs) are constructed to simulate the many-to-many association, and they are applied widely in many fields. It is important to explore the global stability of the periodic solution of MAMNNs. In this paper, MAMNNs with discontinuous activation functions and mixed time-varying delays are considered. Firstly, we investigate the conditions for the existence of the periodic solution by using the Mawhin-like coincidence theorem, and a special connecting weight matrix is constructed to prove the existence of the periodic solution. Secondly, the uniqueness and global exponential stability of the periodic solution are explored for the non-self-connected system by introducing a Lipschitz-like condition. Finally, numerical simulations are given to illustrate the effectiveness of our main results.
关键词 ：
discontinuous activation functions discontinuous activation functions global exponential stability global exponential stability mixed time‐ mixed time‐ multidirectional associative memory neural networks multidirectional associative memory neural networks periodic solution periodic solution varying delays varying delays
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| GB/T 7714 | Zhang, Yan , Qiao, Yuanhua , Duan, Lijuan et al. Periodic dynamics of multidirectional associative neural networks with discontinuous activation functions and mixed time delays [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (10) : 4570-4588 . |
| MLA | Zhang, Yan et al. "Periodic dynamics of multidirectional associative neural networks with discontinuous activation functions and mixed time delays" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 31 . 10 (2021) : 4570-4588 . |
| APA | Zhang, Yan , Qiao, Yuanhua , Duan, Lijuan , Miao, Jun , Zhang, Jiajia . Periodic dynamics of multidirectional associative neural networks with discontinuous activation functions and mixed time delays . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (10) , 4570-4588 . |
| 导入链接 | NoteExpress RIS BibTex |
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 ：
Context Context Meta-learning Meta-learning Semantic-segmentation Semantic-segmentation Zero-shot learning Zero-shot 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 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we propose a deep multimodal feature learning (DMFL) network for RGB-D salient object detection. The color and depth features are firstly extracted from low level to high level feature using CNN. Then the features at the high layer are shared and concatenated to construct joint feature representation of multi-modalities. The fused features are embedded to a high dimension metric space to express the salient and non-salient parts. And also a new objective function, consisting of cross-entropy and metric loss, is proposed to optimize the model. Both pixel and attribute level discriminative features are learned for semantical grouping to detect the salient objects. Experimental results show that the proposed model achieves promising performance and has about 1% to 2% improvement to conventional methods. © 2021 Elsevier Ltd
关键词 ：
Deep learning Deep learning Feature extraction Feature extraction Object detection Object detection Object recognition Object recognition Set theory Set theory Topology Topology
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| GB/T 7714 | Liang, Fangfang , Duan, Lijuan , Ma, Wei et al. A deep multimodal feature learning network for RGB-D salient object detection [J]. | Computers and Electrical Engineering , 2021 , 92 . |
| MLA | Liang, Fangfang et al. "A deep multimodal feature learning network for RGB-D salient object detection" . | Computers and Electrical Engineering 92 (2021) . |
| APA | Liang, Fangfang , Duan, Lijuan , Ma, Wei , Qiao, Yuanhua , Miao, Jun . A deep multimodal feature learning network for RGB-D salient object detection . | Computers and Electrical Engineering , 2021 , 92 . |
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摘要 ：
In this paper, a type of fractional-order quaternion-valued neural networks (FOQVNNs) with leakage and time-varying delays is established to simulate real-world situations, and the global Mittag-Leffler stability of the system is investigated by using the non-decomposition method. First, to avoid decomposing the system into two complex-valued systems or four real-valued systems, a new sign function for quaternion numbers is introduced based on the ones for real and complex numbers. And two novel lemmas for quaternion-valued sign function and Caputo fractional derivative are established in quaternion domain, which are used to investigate the stability of FOQVNNs. Second, a concise and flexible quaternion-valued state feedback controller is directly designed and a novel 1-norm Lyapunov function composed of the absolute values of real and imaginary parts is established. Then, based on the designed quaternion-valued state feedback controller and the proposed lemmas, some sufficient conditions are given to ensure the global Mittag-Leffler stability of the system. Finally, a numerical simulation is given to verify the theoretical results. (C) 2021 Elsevier Ltd. All rights reserved.
关键词 ：
Fractional-order Fractional-order Leakage delay Leakage delay Mittag-Leffler stability Mittag-Leffler stability Quaternion-valued neural networks Quaternion-valued neural networks Time-varying delay Time-varying delay
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| GB/T 7714 | Yan, Hongyun , Qiao, Yuanhua , Duan, Lijuan et al. Novel methods to global Mittag-Leffler stability of delayed fractional-order quaternion-valued neural networks [J]. | NEURAL NETWORKS , 2021 , 142 : 500-508 . |
| MLA | Yan, Hongyun et al. "Novel methods to global Mittag-Leffler stability of delayed fractional-order quaternion-valued neural networks" . | NEURAL NETWORKS 142 (2021) : 500-508 . |
| APA | Yan, Hongyun , Qiao, Yuanhua , Duan, Lijuan , Miao, Jun . Novel methods to global Mittag-Leffler stability of delayed fractional-order quaternion-valued neural networks . | NEURAL NETWORKS , 2021 , 142 , 500-508 . |
| 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 ：
Annotations Annotations exemplar mechanism exemplar mechanism Feature extraction Feature extraction Motion segmentation Motion segmentation Object segmentation Object segmentation Spatiotemporal phenomena Spatiotemporal phenomena spatiotemporal saliency spatiotemporal saliency Task analysis Task analysis Training Training video object segmentation video object segmentation Weakly supervised learning Weakly supervised learning
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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 . |
| 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 ：
deep learning deep learning fusion networks fusion networks HHT HHT multimodal physiological signals multimodal physiological signals sleep stage classification sleep stage classification
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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 . |
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摘要 ：
Convolutional neural networks (CNNs) have shown unprecedented success in object representation and detection. Nevertheless, CNNs lack the capability to model context dependencies among objects, which are crucial for salient object detection. As the long short-term memory (LSTM) is advantageous in propagating information, in this paper, we propose two variant LSTM units for the exploration of contextual dependencies. By incorporating these units, we present a context-aware network (CAN) to detect salient objects in RGB-D images. The proposed model consists of three components: feature extraction, context fusion of multiple modalities and context-dependent deconvolution. The first component is responsible for extracting hierarchical features in color and depth images using CNNs, respectively. The second component fuses high-level features by a variant LSTM to model multi-modal spatial dependencies in contexts. The third component, embedded with another variant LSTM, models local hierarchical context dependencies of the fused features at multi-scales. Experimental results on two public benchmark datasets show that the proposed CAN can achieve state-of-the-art performance for RGB-D stereoscopic salient object detection. © 2020
关键词 ：
Benchmarking Benchmarking Convolutional neural networks Convolutional neural networks Long short-term memory Long short-term memory Object detection Object detection Object recognition Object recognition Stereo image processing Stereo image processing
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| GB/T 7714 | Liang, Fangfang , Duan, Lijuan , Ma, Wei et al. Context-aware network for RGB-D salient object detection [J]. | Pattern Recognition , 2021 , 111 . |
| MLA | Liang, Fangfang et al. "Context-aware network for RGB-D salient object detection" . | Pattern Recognition 111 (2021) . |
| APA | Liang, Fangfang , Duan, Lijuan , Ma, Wei , Qiao, Yuanhua , Miao, Jun , Ye, Qixiang . Context-aware network for RGB-D salient object detection . | Pattern Recognition , 2021 , 111 . |
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