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Adaptive Occlusion Face Recognition Feature Fusion Network Based on Precise Guidance Mask Extraction

Journal of Shanghai Jiaotong University (science) 2025年 1-12页

作者： Ye, Jihua Zhang, Lei Wang, Chao Tu, Qixuan Zheng, Rehua Huang, Huiyuan Jiang, Aiwen School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal University Nanchang330022 China State International Science & Technology Cooperation Base of Networked Supporting Software Jiangxi Normal University Nanchang330022 China

The uncertainty in the position and size of occluding objects greatly affects the extraction of identity features in facial recognition, which is a challenge that existing methods fail to effectively address. To tackle this issue, this paper introduces a novel feature fusion network for occluded facial recognition that adaptively handles arbitrary occlusions and accurately extracts occlusion information masks. The network effectively mitigates the impact of occlusions on facial identity features through a feature fusion module. The proposed method employs an occlusion classification module and an occlusion classification loss to precisely guide the extraction of accurate occlusion masks. Subsequently, an occlusion-aware hybrid attention module is introduced, which focuses on the relationship between facial features and occlusion masks across channel and spatial dimensions. This fusion strategy enables the extraction of facial identity features that are resilient to occlusions. Experimental results indicate that the proposed method holds a significant advantage in handling facial recognition tasks with arbitrary occlusions. On the LFW and MF1 synthetic occlusion datasets, the average accuracy rates reached 93.19% and 72.57%, respectively, showing improvements of 1.7 and 2.39 percent points over mainstream methods. © Shanghai Jiao Tong University 2025.

关键词： A feature fusion occluded facial recognition occlusion mask occlusion perception TP391.41

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GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

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Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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Internet of Things (IoT) in Smart Cities: A Statistical Survey 2

Internet of Things (IoT) in Smart Cities: A Statistical Surv...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Iqbal, Muhammad Waseem Khaliq, Khowla Al-Dmour, Nidal A. Aqeel, Muhammad Ali, Nasir Hamid, K. Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Sciecnce Lahore54000 Pakistan Mutah University College of Engineering Department of Computer Engineering Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

关键词： Internet of things

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Chinese Medical Word Embedding Based on Transfer Learning 3

Chinese Medical Word Embedding Based on Transfer Learning

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2022

作者： Zhou, Menglin Gong, Kecun Chengdu University of Information Technology School of Software Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Word embedding has been successfully applied to Natural Language Processing (NLP) tasks. In this paper, we learn Chinese medical word embeddings in different ways and use the same corpus to evaluate the quality of the word embeddings. Firstly, we perform word embedding training through target domain data combined with data from different domains to achieve word embedding sharing and enrich the semantic information of word embeddings;Secondly, static word embedding and dynamic word embedding are combined to solve semantic ambiguity by adding dynamic word embedding. Finally, word embedding sharing combined with parameter migration is used to reduce the use of training data and adjust word embedding. Furthermore, we evaluate the quality of word embeddings obtained by different models through three metrics: word analogy, word embedding visualization, and named entity recognition task. By comparing the quality of word embedding in different models, we found that our proposed method improved in each evaluation result. © 2022 IEEE.

关键词： Visualization

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Dual-Modes Grasping of SMA-Based Variable-Stiffness Soft Gripper

Dual-Modes Grasping of SMA-Based Variable-Stiffness Soft Gri...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhao, Lei Zhao, Hanyuan Feng, Zhiyu Wang, Sicheng Liu, Huaping Sun, Fuchun Fang, Bin Yin, Jianqin Tsinghua University Department of Computer Science and Technology Beijing100083 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing1000876 China School of Software Beijing100083 China Tandon School of Engineering New York University China

ISBN: (纸本)9781665481090

In this article, we have designed a novel variable-stiffness soft gripper that combines the advantages of both flexible and rigid grippers. It is capable of performing two different tasks: pinching and enveloping. The soft gripper comprises spring steel sheets, memory alloy sheets, tendons, and servo motors, offering the advantages of low cost and simple structure. This variable-stiffness soft gripper can freely switch between the pinching and enveloping by heating the memory alloy sheets and controlling the tendons with servo motors. Finally, to validate the effectiveness of the soft gripper and the algorithm, we constructed experimental platforms for autonomous grasping in virtual and real environments. Experimental results demonstrate that our variable-stiffness soft gripper exhibits excellent grasping performance, stably pinching, and enveloping objects of various shapes and materials, and the achieved grasping success rate is 90%, surpassing the performance of using only precision pinching or enveloping. © 2024 IEEE.

关键词： Grippers

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Efficient Fingerprint Database Organization for Optimized Recognition

Efficient Fingerprint Database Organization for Optimized Re...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Jeffrey Jeeva Dhas Adhip Koshy Nishit Dua D. Ajitha Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

A popular biometric identification method, renowned for its dependability and individuality in personal identification, is fingerprint recognition. This article presents an efficient fingerprint identification system that significantly reduces processing times for 100,000 fingerprints from 8 minutes to less than 5 seconds by utilizing multithreading, ANSI 378 templates, and Source AFIS. The system overcomes the time-consuming image-to-template conversion procedure and increases overall storage efficiency by utilizing parallel processing and preformatted templates (ANSI/ISO). The fingerprint matching system incorporates Directional Field, CNN models for identifying fingerprint types, and Finger Code methods to classify fingerprints, improving precision and efficiency.

关键词： Technological innovation Multithreading Law enforcement System performance Organizations Machine learning Fingerprint recognition Biometric identification Real-time systems Informatics

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DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

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Journal of Internet Technology 2024年第1期25卷 79-86页

作者： Yang, Zhichao Kong, Yan Hsia, Chih-Hsien School of Computer and Software Nanjing University of Information Science and Technology China Department of Computer Science and Information Engineering National Ilan University Taiwan Department of Business Administration Chaoyang University of Technology Taiwan

In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Reinforcement learning

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ReLoc:Indoor Visual Localization with Hierarchical Sitemap and View Synthesis

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Journal of computer science & Technology 2021年第3期36卷 494-507页

作者： Hui-Xuan Wang Jing-Liang Peng Shi-Yi Lu Xin Cao Xue-Ying Qin Chang-He Tu School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information Science and Engineering University of JinanJinan 250022China School of Software Shandong UniversityJinan 250101China

Indoor visual localization,i.e.,6 Degree-of-Freedom camera pose estimation for a query image with respect to a known scene,is gaining increased attention driven by rapid progress of applications such as robotics and augmented ***,drastic visual discrepancies between an onsite query image and prerecorded indoor images cast a significant challenge for visual *** this paper,based on the key observation of the constant existence of planar surfaces such as floors or walls in indoor scenes,we propose a novel system incorporating geometric information to address issues using only pixelated *** the system implementation,we contribute a hierarchical structure consisting of pre-scanned images and point cloud,as well as a distilled representation of the planar-element layout extracted from the original dataset.A view synthesis procedure is designed to generate synthetic images as complementary to that of a sparsely sampled ***,a global image descriptor based on the image statistic modality,called block mean,variance,and color(BMVC),was employed to speed up the candidate pose identification incorporated with a traditional convolutional neural network(CNN)*** results on a popular benchmark demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of visual localization validity and accuracy.

关键词： visual localization planar surface statistic information view synthesis

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IIB-DDI: Invariant Information Bottleneck Theory for Out-of-Distribution Drug-Drug Interaction Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第3期22卷 1009-1022页

作者： Jiahui Zhang Shuai Zhang Xuqiang Li Di Wu Sihan Wang Limin Li Wenjie Du Yang Wang School of Software Engineering University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China

Rapid and accurate identification of drug-drug interactions (DDIs) among multiple medications is crucial for various medical treatments, and clinical therapies. Currently, the increasing significance of molecular substructure interactions in DDI prediction has become a consensus. However, due to the uneven distribution of substructures in molecules and most methods do not consider the differences in substructure distributions, models may rely on spurious substructure relationships, thereby weakening their out-of-distribution (OOD) generalization ability. To address this challenge, we propose an OOD-DDI framework called IIB-DDI. Specifically, information bottleneck theory is initially utilized to extract core subgraphs of drug pairs. Then, considering the diversity and unknown nature of environments, we introduce the vector quantization to design an environment codebook, where the potential environments in the dataset are clustered into a specified number of categories. Subsequently, we position the extracted core subgraphs under various latent environmental factors to attain invariant core substructures (rationales). Additionally, the learned environmental distribution also could be acted as noise injection for optimizing mutual information, achieving a smoother and more stable training curve, thereby leading to lower loss. Extensive experiments conducted on real-world DDI datasets demonstrate the superiority of our model over state-of-the-art baselines.

关键词： Drugs Noise Training Predictive models Data mining Biological system modeling Vector quantization Mutual information Deep learning Accuracy

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Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 614-625页

作者： Xu, Xu Chen, Junxin Lv, Wenrui Wang, Wei Zhang, Yushu Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China

Manipulated images are flooding our daily lives, which poses a threat to social security. Recently, many studies have focused on image tampering detection. However, they have poor performance on independent validation due to differences in image scenes and tampering methods. The key question is how to design a network that is able to adaptively enhance the tampering information and suppress the generalization features during training. To this end, we propose a dual-branch network with a frequency adaptation paradigm and a feature fusion module for robust tampering image detection. First, this paradigm is designed to adaptively highlight tampering features through frequency conversion and learnable weight. Second, a feature fusion module is developed to filter redundant features and dynamically fuse two-branch features. Experiments on eight typical datasets demonstrate that our model has advantages over state-of-the-art algorithms, and our paradigm can well empower semantic segmentation networks for tampering detection. © 2024 IEEE.

关键词： Semantic Segmentation

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