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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jing, Heming Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Fudan University Academy for Engineering and Technology China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics China School of Information Technology Deakin University Australia

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Video captioning aims at generating a natural language de-scription for a given video clip including not only salient sce-narios but also contextual scenarios. The former reveal the highlight of a video and are usually the focus of most existing captioning methods. The latter, however, are not well ex-plored and even ignored easily, though they may provide cer-tain detailed and latent information that can help with a better understanding of the video. To effectively exploit the infor-mation contained in both, a novel video captioning network is proposed. It has two key modules: Cross-Modality Selection (CMS) and Saliency-Context Adaptive Decoder (SCAD). Specifically, CMS mainly focuses on utilizing the semantic information to distinguish saliency and context. Meanwhile, SCAD adaptively identifies both the saliency and context to generate more detailed and precise captions. Experiments on two benchmark datasets, i.e., MSVD and MSR-VTT, demon-strate the effectiveness of our model through the comparison with state-of-the-art methods. © 2022 IEEE.

关键词： Semantics

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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua Science and technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Graph external attention enhanced transformer 24

Graph external attention enhanced transformer

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianqing Liang Min Chen Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan Shanxi China

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA)--a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https://***/icm1018/GEAET.

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Wearable and interactive multicolored photochromic fiber display

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Light(Science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell 3

Cascaded mixed pyramid pooling for surface defect segmentati...

引用

3rd International Conference on Electronic information Engineering, Big Data, and computer technology, EIBDCT 2024

作者： Wang, Yaokun Fu, Xiaowei Li, Xi Xu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510680449

To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification

arXiv

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arXiv 2024年

作者： Ji, Zhangjian Cheng, Donglin Feng, Kai the Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Due to some complex factors (e.g., occlusion, pose variation and diverse camera perspectives), extracting stronger feature representation in person re-identification remains a challenging task. In this paper, we proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. Different from the general transformer-based person re-identification models, we designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. In order to train the contrastive learning branch, we also proposed a novel random rectangle mask strategy to simulate the occlusion in real scenes, so as to enhance the feature representation for occlusion. Finally, we utilized the joint-training loss function to integrate the advantages of supervised learning with ID tags and self-supervised contrastive learning without negative samples, which can reinforce the ability of our model to excavate stronger discriminative features, especially for occlusion. Extensive experimental results on several benchmark datasets show our proposed model obtains superior Re-ID performance consistently and outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy. © 2024, CC BY-NC-SA.

关键词： Self-supervised learning

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Test-Time Training with Invariant Graph Learning for Out-of-Distribution Generalization

SSRN

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SSRN 2024年

作者： Pi, Li Li, Jiaxing Song, Lei Yang, Guanyu Chen, Yang Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Jiangsu Province Nanjing China

Graph Neural Networks (GNNs) frequently do not perform optimally on out-of-distribution (OOD) data. Previous research on graph OOD generalization suggests extracting invariant subgraphs, which provide crucial categorical information even under test distributions. However, this strategy is not optimal due to two issues. Firstly, there is a significant distribution shift between the test and training sets, which may result in incorrect invariant subgraphs being extracted during the test phase. Secondly, the extraction of subgraphs can lead to the loss of important structural information. To address these issues, we propose a novel two-stage training paradigm named \textbf{I}nvariant \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{T}raining (\textbf{IGT3}), which uses the training paradigm called Test-Time Training (TTT). We also propose invariant graph recognition to learn key parts of the graph without destroying the original graph structure. In addition, we introduce a multi-level graph contrastive learning method as an auxiliary self-supervised task. To prevent TTT failure, we use the feature constraint to enhance the robustness of the training process. We conduct experiments on seven common graph classification OOD benchmark datasets. The results demonstrate that IGT3 outperforms state-of-the-art (SOTA) methods by an average of 4.2\%, confirming the effectiveness of each module. The paper code is available at https://***/r/IGT3-3281/ © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

引用

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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Transformer Helps Gnns Express Better Via Distillation of Long-Range Dependencies

SSRN

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SSRN 2024年

作者： Li, Jiaxing Zhang, Xiaotong Zhang, Ke Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Jiangsu Province Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Graph neural networks (GNNs) are powerful architectures for graph representation learning. However, GNNs based on message-passing cannot make fully use of the valuable long-range dependencies in graph-structured data. Recently, researchers have designed Transformer-based graph learning models to expand the receptive field to capture long-range dependencies between pairwise nodes. Although the preliminary performance of the graph Transformer is encouraging and even surpasses GNNs, the quadratic complexity of the self-attention leads to much longer inference time than GNNs, which limits the deployment in practical applications. To address this issue, we propose a knowledge distillation framework to transfer long-range dependencies captured by graph Transformer to GNNs. Our framework extracts knowledge from a pre-trained graph Transformer teacher and transfer it to a GNN-based student. To better transfer the long-range dependencies, we further design a long-range dependencies transfer module, in which the intermediate graph representations of the graph Transformer and GNNs are extracted as distributions. Then we minimize the distance between the two distributions, thereby achieving the long-range dependencies transfer more accurately. The proposed method can be extended to any graph Transformer and GNNs. We evaluate our proposed method on a large number of graph classification benchmark datasets with different GNN-based baselines, and the experimental results demonstrate that our method can obtain the performance close to graph Transformer with faster inference speed and smaller memory usage. © 2024, The Authors. All rights reserved.

关键词： Distillation

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