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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Performance Verification of State Feedback H∞ State Molecular Controller Via Chemical Reaction Networks

Performance Verification of State Feedback H∞ State Molecul...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Jingwang Yao Xing'An Wang Hui Lv Yang Li Wujie Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China College of Environmental and Chemical Engineering Dalian University Dalian China Dalian Chivy Biotechnology Co. Ltd Dalian PR China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

An important goal in synthetic biology is in the design and implementation of control strategies embedded in biochemical reaction systems to enable control of the process at the molecular level. The problem of set-point tracking and perturbation disturbances in the biochemical reaction process can be solved by a state feedback control strategy and a more stable and better dynamic performance of synthetic controllers. First, it is shown how a set of CRNs can provide state feedback to the biomolecular implementation of the H ∞ controller. Then, to facilitate the modularity of biochemical process design, CRNs of trigonometric functions and repetitive sequence step signals are defined for the first time as input signals to the controller. Finally, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from several perspectives, such as setpoint tracking and external perturbations. The results show that the state feedback H ∞ molecular controller outperforms the conventional linear controller because it has a faster tracking response and better perturbation rejection performance.

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Speech Emotion Recognition Based on Three-Channel Feature Fusion of CNN and BiLSTM 2020

Speech Emotion Recognition Based on Three-Channel Feature Fu...

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4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020

作者： Huang, Lilong Dong, Jing Zhou, Dongsheng Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781450376587

It is important for human-computer natural interaction that the computer can quickly and effectively identify the interactor's emotional state. We proposed a three-channel feature fusion algorithm based on Convolutional Neural Network and Bidirectional Long Short-Term Memory Network for speech emotion recognition. Mel-Frequency Cepstral Coefficients and its first and second-order difference features were input into three channels and trained respectively for exploiting the emotional information contained in the dynamic features. By splicing and fusing the features obtained by three channels, the recognition rates of proposed algorithm reached 96.562% and 92.34% on CASIA corpus with six emotional states and on EMO-DB corpus with seven emotional states respectively. © 2020 ACM.

关键词： Long short-term memory

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Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint 5

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

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5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Ji, Lu Liu, Rui Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728168968

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art. © 2020 IEEE.

关键词： Least squares approximations

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Acoustic Scene Classification by Enhanced High-Frequency Weak Signal Characteristic Spectrum 19

Acoustic Scene Classification by Enhanced High-Frequency Wea...

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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Wenjie Hao Lasheng Zhao Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University

ISBN: (纸本)9781450376570

In the acoustic scene classification task, the method of using mel-spectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information about some acoustic scenes. The paper improves the mel-spectrogram and its generation algorithm. Including: I. For the sensitivity of the acoustic scene to high-frequency acoustic signals, the paper changes the filter design method of Mel Frequency Cepstrum Coefficient (MFCC). This method preserves more high frequency information by applying the equal-height triangular filter banks and increasing the number of the filters. II. Based on the previous step, an enhancement algorithm is proposed for the problem of the lack of high-frequency weak signals in the characteristic spectrum. The algorithm performs nonlinear mapping on the mel-spectrogram, which makes the transformed high-frequency weak signal feature information more obvious. The algorithm is verified by DCASE 2018 acoustic scene classification dataset and LITIS ROUEN dataset. The experimental results demonstrate the effectiveness of the proposed algorithm.

关键词： MFCC weak signal feature extraction Acoustic scene classification

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Acoustic scene classification by enhanced high- frequency weak signal characteristic spectrum 8

Acoustic scene classification by enhanced high- frequency we...

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8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Hao, Wenjie Zhao, Lasheng Zhang, Qiang Key Laboratory of Advanced Design Intelligent Computing Ministry of Education Dalian University China

ISBN: (纸本)9781450376570

In the acoustic scene classification task, the method of using melspectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information about some acoustic scenes. The paper improves the mel-spectrogram and its generation algorithm. Including: I. For the sensitivity of the acoustic scene to highfrequency acoustic signals, the paper changes the filter design method of Mel Frequency Cepstrum Coefficient (MFCC). This method preserves more high frequency information by applying the equal-height triangular filter banks and increasing the number of the filters. II. Based on the previous step, an enhancement algorithm is proposed for the problem of the lack of highfrequency weak signals in the characteristic spectrum. The algorithm performs nonlinear mapping on the mel-spectrogram, which makes the transformed high-frequency weak signal feature information more obvious. The algorithm is verified by DCASE 2018 acoustic scene classification dataset and LITIS ROUEN dataset. The experimental results demonstrate the effectiveness of the proposed algorithm. © 2019 Association for computing Machinery.

关键词： Spectrographs

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Active interaction strategy generation for human-robot collaboration based on trust

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Visual computing for industry, biomedicine, and art 2025年第1期8卷 16页

作者： Yujie Guo Pengfei Yi Xiaopeng Wei Dongsheng Zhou Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian 116622 China. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China. Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian 116622 China. zhouds@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China. zhouds@***.

In human-robot collaborative tasks, human trust in robots can reduce resistance to them, thereby increasing the success rate of task execution. However, most existing studies have focused on improving the success rate of human-robot collaboration (HRC) rather than on enhancing collaboration efficiency. To improve the overall collaboration efficiency while maintaining a high success rate, this study proposes an active interaction strategy generation for HRC based on trust. First, a trust-based optimal robot strategy generation method was proposed to generate the robot's optimal strategy in a HRC. This method employs a tree to model the HRC process under different robot strategies and calculates the optimal strategy based on the modeling results for the robot to execute. Second, the robot's performance was evaluated to calculate human's trust in a robot. A robot performance evaluation method based on a visual language model was also proposed. The evaluation results were input into the trust model to compute human's current trust. Finally, each time an object operation was completed, the robot's performance evaluation and optimal strategy generation methods worked together to automatically generate the optimal strategy of the robot for the next step until the entire collaborative task was completed. The experimental results demonstrates that this method significantly improve collaborative efficiency, achieving a high success rate in HRC.

关键词： Human-robot collaboration Human-robot trust Tree Visual language model

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Hand Pose Estimation Using Convolutional Neural Networks and Support Vector Regression 12th

Hand Pose Estimation Using Convolutional Neural Networks and...

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Dong, Yufeng Lu, Jian Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

ISBN: (纸本)9783030237110

In order to improve the accuracy of hand pose estimation from a depth image, a method based on convolutional neural network (CNN) is proposed in this paper. First of all, we modify the structure of traditional CNN to recognize the 3D joint locations from a depth image. By appending some shortcuts between layers, the proposed network increases the correlation between the front and back layers. This structure can avoid the information loss caused by the simple layer-by-layer transmission, and can improve the estimation accuracy effectively. Afterwards, the estimated joint locations continue to be inputted into a support vector regression (SVR) phase. The use of SVR can introduce the constraint of local joint information, which can get rid of those abnormal estimations further. Extensive experiments show that our method enables significant performance improvement over the-state-of-arts in the accuracy of hand pose estimation. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Explore Contextual Information for 3D Scene Graph Generation

arXiv

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arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Aspect-Level Sentiment Classification of Chinese Patient Comments Based on Pre-trained Sentiment Embedding

Aspect-Level Sentiment Classification of Chinese Patient Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongxue Shan Zhaoqian Zhong Chao Che Bo Jin Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781665429825

With the development of information technology, online health care service platforms have collected a large amount of patient comment information. Through fine-grained sentiment classification of this information, we can provide references for patients to seek medical treatment and help doctors understand their work. Therefore, we proposed a model that integrated pre-trained emotional information and semantic information at the word and character level for aspect-level sentiment classification. Specifically, we employed adversarial learning for training sentiment word embeddings and the two-layer bidirectional long short-term memory network to extract the sentiment embedding of the entire sentence in a specific aspect. We also combined the pre-trained sentiment feature vector with the structured semantic information by linear weighting and the multi-head self-attention mechanism, enabling the model to pay more attention to the information most relevant to a given aspect category. We performed experiments on the Chinese patient comments data set constructed by our research team and the proposed model outperformed the state-of-the-art methods, which proved the effectiveness of the proposed model for aspect-level sentiment classification of Chinese patient comments.

关键词： Training Biological system modeling Conferences Semantics Bit error rate Medical treatment Feature extraction

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