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17th Asian Conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Prospects and Challenges in COVID-19 Study: A Review Based on Influencing Factors and Analysis Methods 23

Prospects and Challenges in COVID-19 Study: A Review Based o...

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6th International Conference on Big data Technologies, ICBDT 2023

作者： Liu, Lisai Zhu, Dashuang Zheng, Tianlong Niu, Yiheng Zhang, Zhao Zhang, Zihan Zhu, Xu Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9798400707667

The unprecedented Coronavirus Disease 2019 had spread and diffused in many countries and regions worldwide, posing a serious threat to people's health and lives. This review presents a systematic analysis and summary of representative research on the role of AI technology in the prediction and containment of COVID-19 and influencing factors of its transmission. To further explore the influences of multiple factors on the incidence and transmission of COVID-19, we used total cases per million (TCPM) and total deaths per million (TDPM) as dependent variables and influencing factors as explanatory variables to conduct multiple linear regression, and constructed four training models: xgboost model (XGBR), random forest regenerator (RFR), extra trees regenerator (ETR), and gradient boosting regenerator (GBR) to predict the test data. This study highlights the contribution and application prospects of AI related technologies in the study of the spread of COVID-19, which will help deepen the global understanding of the COVID-19 pandemic, and provide a reference for curbing the wanton spread of COVID-19 and other diseases again. © 2023 ACM.

关键词： COVID-19

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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Large Model for Small data: Foundation Model for Cross-Modal RF Human Activity Recognition 24

Large Model for Small Data: Foundation Model for Cross-Modal...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706974

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi's generalizability across various environments. © 2024 Copyright is held by the owner/author(s).

关键词： Zero-shot learning

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Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network

Advancing Event Causality Identification via Heuristic Seman...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Haoran Gao, Qiang Wu, Hongmei Huang, Li College of Computer Science Sichuan University China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics China Engineering Research Center of Intelligent Finance Ministry of Education Southwestern University of Finance and Economics China

ISBN: (纸本)9798891761643

Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://***/hrlics/SemDI. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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The Assessment of Clustering on Weighted Network with R Package clustAnalytics 24

The Assessment of Clustering on Weighted Network with R Pack...

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24th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2022

作者： Arratia, Argimiro Renedo-Mirambell, Martí Intelligent Data Science and Artificial Intelligence Research Center Department of Computer Sciences Polytechnical University of Catalonia Barcelona Spain

ISBN: (纸本)9781643683263

We present clustAnalytics, an R package available now on CRAN, which provides methods to validate the results of clustering algorithms on unweighted and weighted networks, particularly for the cases where the existence of a community structure is unknown. clustAnalytics comprises a set of criteria for assessing the significance and stability of a clustering. To evaluate clusters' significance, clustAnalytics provides a set of community scoring functions, and systematically compares their values to those of a suitable null model. For this it employs a switching model to produce randomized graphs with weighted edges. To test for clusters' stability, a non parametric bootstrap method is used, together with similarity metrics derived from information theory and combinatorics. In order to assess the effectiveness of our clustering quality evaluation methods, we provide methods to synthetically generate networks (weighted or not) with a ground truth community structure based on the stochastic block model construction, as well as on a preferential attachment model, the latter producing networks with communities and scale-free degree distribution. © 2022 The authors and IOS Press.

关键词： Information theory

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CRA: Text to Image Retrieval for Architecture Images by Chinese CLIP 7

CRA: Text to Image Retrieval for Architecture Images by Chin...

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7th International Conference on Machine Vision and Information Technology, CMVIT 2023

作者： Wang, Siyuan Yan, Yuyao Yang, Xi Huang, Kaizhu Xi'An Jiaotong-Liverpool University Department of Intelligent Science Suzhou China Suzhou Bitspring Technology Co Limited Suzhou China Duke Kunshan University Data Science Research Center Suzhou China

ISBN: (纸本)9781665464857

Text-To-image retrieval is revolutionized since the Contrastive Language-Image Pre-Training model was proposed. Most existing methods learn a latent representation of text and then align its embedding with the corresponding image's embedding from an image encoder. Recently, several Chinese CLIP models have supported a good representation of paired image-Text sets. However, adapting the pre-Trained retrieval model to a professional domain still remains a challenge, mainly due to the large domain gap between the professional and general text-image sets. In this paper, we introduce a novel contrastive tuning model, named CRA, using Chinese texts to retrieve architecture-related images by fine-Tuning the pre-Trained Chinese CLIP. Instead of fine-Tuning the whole CLIP model, we engage the Locked-image Text tuning (LiT) strategy to adapt the architecture-Terminology sets by tuning the text encoder and freezing the pre-Trained large-scale image encoder. We further propose a text-image dataset of architectural design. On the text-To-image retrieval task, we improve the metric of R@20 from 44.92% by the original Chinese CLIP model to 74.61% by our CRA model in the test set. © 2023 IEEE.

关键词： Image enhancement

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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