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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiang Bai, Xiao Wang, Chen School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China

ISBN: (纸本)9783031463075

The deep multi-view stereo (MVS) approaches generally construct 3D cost volumes to regularize and regress the depth map. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we presented an multi-stage iterative depth map estimation method for MVS. In our network, the cost volume is iteratively processed by lightweight 2D convolution based GRU modules, and the multi-stage coarse-to-fine structure is adopted to speed up the depth estimation process. To further improve the 3D reconstruction quality, we make improvements from two different perspectives of adaptive cost aggregation: a view-adaptive weighting module is proposed to account for the occlusion problem in cost volume fusion, and a spatial-adaptive deformable geometric feature encoding module is introduced to the cost volume feature encoding before feeding into GRUs for stronger modeling capability. Experiments on the DTU dataset demonstrated the effectiveness of the proposed network in accuracy with remarkable efficiency performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Iterative methods

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Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks

Deep Bayesian Image Set Classification Approach for Defense ...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Mirnateghi, Nima Shah, Syed Afaq Ali Bennamoun, Mohammed Edith Cowan University Center for Ai and Machine Learning Australia The University of Western Australia School of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350382204

Deep learning has become an integral part of various pattern recognition and computer vision systems in recent years due to its outstanding achievements in object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to being fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in physical-world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image-set-based adversarial defense approach. Image set classification has shown exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defense framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defense methods and single-shot recognition task. The empirical results demonstrate superior performance on the CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets. Our code is available at https://***/ai-voyage/***. © 2023 IEEE.

关键词： Object recognition

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Deep Semi-Supervised Learning with Fine-Grained Dynamic Weights and Pseudo-Label Constraints

Deep Semi-Supervised Learning with Fine-Grained Dynamic Weig...

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2023 International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2023

作者： Li, Kuan Lian, Qianzhi Gao, Can Dongguan University of Technology School of Cyberspace Security Dongguan China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350311297

Semi-supervised learning is comonly trained using a large amount of unlabeled data and a small amount of labeled data. Existing works have achieved excellent results through consistent regularization methods. However, when designing the loss function, they set a loss function for labeled and unlabeled data separately, ignoring the connection between labeled and unlabeled data. Furthermore, unlabeled data all share the same weight, but the weights of different unlabeled data should be different. Based on these observations, a pair loss is proposed in this paper to strengthen the connection between labeled and unlabeled data;in addition, a dynamic weight is designed for each unlabeled data based on EMA (Exponential Moving Average) when calculating the loss function. Finally, a deep semi-supervised learning method called DWPC is proposed by combining dynamic weighting and pair loss. Experimental results on several datasets show that the proposed method improves performance in many scenarios. © 2023 IEEE.

关键词： Deep learning

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Detached and Interactive Multimodal Learning 24

Detached and Interactive Multimodal Learning

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fan, Yunfeng Xu, Wenchao Wang, Haozhao Liu, Junhong Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Hubei China School of Software Beihang University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9798400706868

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://***/fanyunfeng-bit/DI-MML. © 2024 Owner/Author.

关键词： Contrastive Learning

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Advancing Efficient Large-Scale Constraint Optimization Problem Solving by Integrating Machine Learning and Automated Reasoning 2

Advancing Efficient Large-Scale Constraint Optimization Prob...

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2nd International Workshop on Spatio-Temporal Reasoning and Learning, STRL 2023

作者： Kong, Shufeng School of Software Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Cornell University Ithaca United States

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Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification 24

Category-Prompt Refined Feature Learning for Long-Tailed Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yan, Jiexuan Huang, Sheng Mu, Nankun Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over *** code is available at https://***/jiexuanyan/CPRFL. © 2024 ACM.

关键词： Semantics

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Enrich Code Search Query Semantics with Raw Descriptions 19th

Enrich Code Search Query Semantics with Raw Descriptions

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Xiangzheng Liu, Jianxun Hu, Haize Liu, Yi School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search can recommend relevant source code according to the development intention (query statement) of the demander, thereby improving the efficiency of software development. In the research of deep code search model, code description is used to replace query sentences for training. However, the heterogeneity existing between the query statement and the code description will seriously affect the accuracy of the code search model. In order to make up for the shortcomings of code search, this paper proposes a sentence-integrated query expansion method—SIQE. Unlike previous query expansion methods that focus on word-level expansion, SIQE uses the entire code description fragment as the source of query expansion. And by learning the mapping relationship between the query statement and the code description, the heterogeneity problem between them is compensated. In order to verify the effect of the proposed model in code search tasks, the article conducts code search experiments and analyzes on two languages: python and java. Experimental results show that, compared with the baseline model, SIQE has higher code search results. Therefore, the SIQE model can effectively improve the search effect of query statements, improve the accuracy of code search, and further improve the development of software engineering. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep learning

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Accurate Reliability Analysis Methods for Approximate Computing Circuits

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Tsinghua science and Technology 2022年第4期27卷 729-740页

作者： Zhen Wang Guofa Zhang Jing Ye Jianhui Jiang Fengyong Li Yong Wang School of the Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Software Engineering Tongji UniversityShanghai 200092China

In recent years,Approximate Computing Circuits(ACCs)have been widely used in applications with intrinsic tolerance to *** the increased availability of approximate computing circuit approaches,reliability analysis methods for assessing their fault vulnerability have become highly *** this study,two accurate reliability evaluation methods for approximate computing circuits are *** reliability of approximate computing circuits is calculated on the basis of the iterative Probabilistic Transfer Matrix(PTM)*** the calculation,the correlation coefficients are derived and combined to deal with the correlation problem caused by fanout *** accuracy and scalability of the two methods are verified using three sets of approximate computing circuit instances and more circuits in Evo Approx8 b,which is an approximate computing circuit open source *** results show that relative to the Monte Carlo simulation,the two methods achieve average error rates of 0.46%and 1.29%and time overheads of 0.002%and 0.1%.Different from the existing approaches to reliability estimation for approximate computing circuits based on the original PTM model,the proposed methods reduce the space overheads by nearly 50%and achieve time overheads of 1.78%and 2.19%.

关键词： Approximate Computing Circuit(ACC) correlation coefficient iterative Probabilistic Transfer Matrix(PTM) reliability

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