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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Hua, Chunsheng Zhang, Wenqian Zhao, Xiaoheng Zhang, Hao Institute Of Intelligent Robots And Pattern Recognition College Of Information Liaoning University Shenyang China

ISBN: (纸本)9798350361445

This study introduces an innovative person re-identification method, TriFusionNet, which cleverly combines 3D reconstruction image features with data augmentation techniques, significantly enhancing performance. We incorporate a 'Physiological information Feature Extraction Branch' to comprehensively capture both the body structure and motion information of persons. The primary challenge addressed is the longstanding negative impact of viewpoint changes on accuracy in person re-identification. Our approach enables the synthesis of more robust and identifiable features in new views. Furthermore, to comprehensively capture person features, we introduce a physiological information feature extraction branch that extracts 15 authentic physiological features from action sequences. These features not only contribute to accuracy but also deepen the understanding of person body status and motion characteristics. The uniqueness of this research lies in going beyond traditional data augmentation methods. Through 3D reconstruction and feature fusion, we address occlusion issues and, through keyframe selection and multi-angle feature fusion, diversify training data more effectively, preserving the body structure and motion information of persons. In summary, TriFusionNet introduces innovative thinking to person re-identification by integrating 3D reconstruction, feature fusion, and physiological information feature extraction. It not only improves performance but also establishes a foundation for more comprehensive extraction of person features, addressing challenges faced by traditional methods. © 2024 IEEE.

关键词： Extraction

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3D Human Pose Estimation with Bone Symmetry and Motion Locality 3

3D Human Pose Estimation with Bone Symmetry and Motion Local...

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3rd International Conference on Electronics and information Technology, EIT 2024

作者： Zhang, Xinyu Hua, Chunsheng Liaoning University Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning Shenyang110036 China

ISBN: (纸本)9798350369151

The bone symmetry and motion locality are two important priors for 3D human pose estimation. For bone symmetry, symmetric bones typically have equal length and higher motion correlation. In terms of motion locality, the motion of the human body conforms to its structure and is temporally continuous. These two priors align with human intuition, but are challenging for Transformer to directly abstract them from data. We propose novel modules, Bone Symmetry Temporal Transformer (BSTT) and Spatial-Temporal Locality Fusion (STLF), to utilize these two priors, and design a new Transformer-based BSMLFormer model. Specifically, BSTT considers the temporal sequences of two symmetric bones as a single new sequence and calculates its global relationships by self-attention mechanism. STLF performs local computations by GCN and convolutions to capture the local dependencies in adjacent joints and adjacent parts. The local-global alternating structure of BSMLFormer allows for consideration of the local information during global calculations. More remarkably, BSMLFormer achieves the state-of-the-art performance with 39.0mm MPJPE on Human3.6M dataset. © 2024 IEEE.

关键词： Motion estimation

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3D Human Pose Estimation with Bone Symmetry and Motion Locality

3D Human Pose Estimation with Bone Symmetry and Motion Local...

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International Conference on Electronics and information Technology (EIT)

作者： Xinyu Zhang Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning University Shenyang China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

关键词： Three-dimensional displays Correlation Computational modeling Pose estimation Stacking Bones Transformers Joints information technology

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TriFusionNet: Comprehensive Adaptation to Viewpoint Changes and Physiological information Integration in 3D-Based Person Re-Identification

TriFusionNet: Comprehensive Adaptation to Viewpoint Changes ...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Chunsheng Hua Wenqian Zhang Xiaoheng Zhao Hao Zhang Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning University Shenyang P.R. China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

This study introduces an innovative person re-identification method, TriFusionNet, which cleverly combines 3D reconstruction image features with data augmentation techniques, significantly enhancing performance. We incorporate a “Physiological information Feature Extraction Branch” to comprehensively capture both the body structure and motion information of persons. The primary challenge addressed is the longstanding negative impact of viewpoint changes on accuracy in person re-identification. Our approach enables the synthesis of more robust and identifiable features in new views. Furthermore, to comprehensively capture person features, we introduce a physiological information feature extraction branch that extracts 15 authentic physiological features from action sequences. These features not only contribute to accuracy but also deepen the understanding of person body status and motion characteristics. The uniqueness of this research lies in going beyond traditional data augmentation methods. Through 3D reconstruction and feature fusion, we address occlusion issues and, through keyframe selection and multi-angle feature fusion, diversify training data more effectively, preserving the body structure and motion information of persons. In summary, TriFusionNet introduces innovative thinking to person re-identification by integrating 3D reconstruction, feature fusion, and physiological information feature extraction. It not only improves performance but also establishes a foundation for more comprehensive extraction of person features, addressing challenges faced by traditional methods.

关键词： Three-dimensional displays Control engineering Training data Feature extraction Data augmentation Physiology Data mining

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Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny 25

Semi-supervised Lightweight Object Detection Method Based on...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Zhilong Gao Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition Liaoning University Shenyang Liaoning China College of Information Liaoning University Shenyang Liaoning China

ISBN: (纸本)9798400713231

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.

关键词： Object Detection

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Visual Prompt Flexible-Modal Face Anti-Spoofing

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2597-2606页

作者： Yu, Zitong Cai, Rizhao Cui, Yawen Liu, Ajian Chen, Changsheng Great Bay University School of Computing and Information Technology Dongguan523000 China Nanyang Technological University ROSE Lab School of EEE 639798 Singapore Hong Kong Polytechnic University Kowloon Hong Kong Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen518060 China

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS (Yu et al. 2023) has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose Visual Prompt flexible-modal FAS (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training. © 2004-2012 IEEE.

关键词： Alignment

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Analysis Model of Terrorist Attacks Based on Big Data 32

Analysis Model of Terrorist Attacks Based on Big Data

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第32届中国控制与决策会议

作者： LIN Zhenkai DOU Yimin LI Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing(University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Institute of Pattern Recognition and Intelligent Systems University of Jinan

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

With the continuous deepening of international anti-terrorism movement,the anti-terrorism has entered a new stage,and it is facing new *** of the new challenges is to extract useful and valuable information from massive data *** anti-terrorism system model based on local shallow information is imperfect,which is not conducive to obtaining accurate prediction *** shortcomings of existing research are the lack of comprehensive analysis and deeper mining of *** order to improve the efficiency and accuracy of the present anti-terrorism system,we propose an effective method for risk assessment and prediction based on machine learning by using Global Terrorism Database(GTD).There are four basic steps:first,we reduce the data dimension through correlation calculation and Singular Value Decomposition(SVD),then,the function is established to rank the harmfulness of terrorist attacks;second,the cascaded network with attention mechanism is used to predict suspects;third,k-means is used to cluster the regions of terrorist attacks,and then we establish a generalized linear regression model to predict the situation of terrorist *** verify the feasibility of the model by comparing with the real *** experimental results show that the proposed method can analyze and predict the information related to terrorist attacks comprehensively and accurately.

关键词： anti-terrorism warning data modeling SVD cascade network with attention mechanism K-means

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VSR++: Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

VSR++: Improving Visual Semantic Reasoning for Fine-Grained ...

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International Conference on pattern recognition

作者： Hui Yuan Yan Huang Dongbo Zhang Zerui Chen Wenlong Cheng Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The College of Automation and Electronic Information Xiangtan University Xiangtan China Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

Image-text matching has made great progresses recently, but there still remains challenges in fine-grained matching. To deal with this problem, we propose an Improved Visual Semantic Reasoning model (VSR++), which jointly models 1) global alignment between images and texts and 2) local correspondence between regions and words in a unified framework. To exploit their complementary advantages, we also develop a suitable learning strategy to balance their relative importance. As a result, our model can distinguish image regions and text words in a fine-grained level, and thus achieves the current state-of-the-art performance on two benchmark datasets.

关键词： Visualization Semantics Benchmark testing Cognition pattern matching

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AMCF-Net: A Novel Adaptive Multi-Channel Fusion Network for Computer-Aided Diagnosis of Lung Nodules in Chest Computed Tomography

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IET Communications 2025年第1期19卷

作者： Nan Wang Yu Gu Lidong Yang Baohua Zhang Jing Wang Xiaoqi Lu Jianjun Li Dahua Yu Ying Zhao Xin Liu Siyuan Tang Qun He Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Digital and Intelligent Industry Inner Mongolia University of Science and Technology Baotou China School of Information and Electronics Beijing Institute of Technology Beijing China College of Information Engineering Inner Mongolia University of Technology Hohhot China School of Computer Science and Technology Baotou Medical College Inner Mongolia University of Science and Technology Baotou China

Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi-channel fusion network (AMCF-Net) is proposed for computer-aided diagnosis of lung nodules. First, a Multi-Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi-scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF-Net, a novel adaptive depth-wise separable convolution with a squeeze-and-excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF-Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1-score of 86.57%, a sensitivity of 86.49%, and a G-mean of 87.72%. Compared with other advanced networks, AMCF-net delivers high-precision lung nodule classification with minimal inference cost. Related codes have been released at: https://***/GuYuIMUST/AMCF-net .

关键词： computer networks convolution health care image processing neural nets

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A Method for Authenticity Identification of Fritillaria Cirrhosa D. Don Based on Deep Learning 4

A Method for Authenticity Identification of Fritillaria Cirr...

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4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Hu, Ke Hu, Pan Cao, Dong Yan, Xin Yu, Xi Liu, Chang Chengdu University College of Information Science and Engineering Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China Research Institute of Big Data Chengdu University China Chengdu Institute of Chinese Herbal Medicine China

ISBN: (纸本)9781728123257

Since the authentic Fritillaria Cirrhosa D. Don resources are scarce due to its high price and valuable medical uses, it is difficult to meet the clinical needs. Therefore, the problem of adulteration in the market is more and more serious. At present, the identification of Fritillaria mainly relies on traditional trait identification, microscopic identification, physical and chemical identification and other methods, which is subjective and requires high practical experience for operators, and the pretreatment work is cumbersome. In this paper, the concept of deep learning is introduced into the identification of Fritillaria for the first time. First, we have created the first multi-angle Fritillaria standard datasets, including Fritillaria Cirrhosa D. Don and Fritillary bulb. We then have proposed a novel deep learning framework SE-DPU to classify Fritillaria datasets, from which we can get the highest classification accuracy. The framework is a hybrid of Squeeze-and-Excitation block (SE unit), U-Net and Dual Path Network (DPN). It reuses the features and explores the new features adaptively recalibrates channel-wise feature responses obtains abundance features while training. Experimental results with Fritillaria Cirrhosa D. Don data have indicated that the proposed method have provided competitive performance. © 2019 IEEE.

关键词： Deep learning

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