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A Point–Set–Domain Image Object Matching Method for Airborne Object Localization

Journal of Internet technology 2025年第3期26卷 303-314页

作者： Liu, Xiaomin Zhao, Runqi Li, Jun-Bao Pan, Jeng-Shyang Zhao, Huaqi The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence and Information Processing Jiamusi University China Faculty of Computing Harbin Institute of Technology China College of Computer Science and Engineering Shandong University of Science and Technology China

Image object localization is an important research direction in the development of intelligent autonomous control systems for unmanned aerial vehicles (UAVs). Major challenges remain, such as cross-view images, large-scale deformation, and multitemporal variation. We propose a point–set–domain matching method to locate objects. First, the property constraints of a point, including sparsity, repeatability, and distinguishability, are combined into a keypoint response used to optimize convolutional neural networks, creating keypoint detector and feature descriptor models. With these models, we can improve the performance of point matching and obtain the corresponding keypoint set accurately. This approach solves the cross-view problem. Second, a spatial transformation model of the corresponding keypoint set is obtained using keypoint-constrained diffeomorphism matching, which can align the spatial location of two images and solve the large-scale deformation problem. Third, an approach combining probability statistics with watershed maximally stable extremal regions is proposed to divide the object image and reference image into several subregions, and then the similarity based on diffeomorphism is employed to localize the object in the UAV image, which solves the multitemporal variation problem. The experimental results show that the proposed method can successfully determine the location of the object in the UAV image. © 2025 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Drones

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Improving Molecular Graph Generation with Flow Matching and Optimal Transport

arXiv

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arXiv 2024年

作者： Hou, Xiaoyang Zhu, Tian Ren, Milong Bu, Dongbo Gao, Xin Zhang, Chunming Sun, Shiwei Key Laboratory of Intelligent Information Processing Institute of Computing Technology School of Computer Science and Technology University of Chinese Academy of Sciences China Central China Research Institute for Artificial Intelligence Technologies China Western Institute of Computing Technology Phil Rivers Technology King Abdullah University of Science and Technology Saudi Arabia

Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in molecular graph design, they often suffer from unstable training and inefficient sampling. To enhance generation performance and training stability, we propose GGFlow, a discrete flow matching generative model incorporating optimal transport for molecular graphs and it incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. Additionally, GGFlow introduces a novel goal-guided generation framework to control the generative trajectory of our model, aiming to design novel molecular structures with the desired properties. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks, outperforming existing baselines and underscoring its effectiveness and potential for wider application. © 2024, CC BY.

关键词： Molecular docking

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A Cross-direction Task Decoupling Network for Small Logo Detection

A Cross-direction Task Decoupling Network for Small Logo Det...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Sujuan Hou Xingzhuo Li Weiqing Min Jiacheng Li Jing Wang Yuanjie Zheng Shuqiang Jiang School of Information Science and Engineering Shandong Normal University Jinan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Logo detection plays an integral role in many applications. However, handling small logos is still difficult since they occupy too few pixels in the image, which burdens the extraction of discriminative features. The aggregation of small logos also brings a great challenge to the classification and localization of logos. To solve these problems, we creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. We first introduce Cross-direction Feature Pyramid (CFP) to realize cross-direction feature fusion by adopting horizontal transmission and vertical transmission. In addition, Multi-frequency Task Decoupling Head (MTDH) decouples the classification and localization tasks into two branches. A multi-frequency attention convolution branch is designed to achieve more accurate regression by combining discrete cosine transform and convolution creatively. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.

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DriveEditor: A Unified 3D information-Guided Framework for Controllable Object Editing in Driving Scenes 39

DriveEditor: A Unified 3D Information-Guided Framework for C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Yiyuan Yan, Zhiying Chen, Liqun Zhou, Jiahuan Yan, Luxin Zhong, Sheng Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)157735897X

Vision-centric autonomous driving systems require diverse data for robust training and evaluation, which can be augmented by manipulating object positions and appearances within existing scene captures. While recent advancements in diffusion models have shown promise in video editing, their application to object manipulation in driving scenarios remains challenging due to imprecise positional control and difficulties in preserving high-fidelity object appearances. To address these challenges in position and appearance control, we introduce DriveEditor, a diffusion-based framework for object editing in driving videos. DriveEditor offers a unified framework for comprehensive object editing operations, including repositioning, replacement, deletion, and insertion. These diverse manipulations are all achieved through a shared set of varying inputs, processed by identical position control and appearance maintenance modules. The position control module projects the given 3D bounding box while preserving depth information and hierarchically injects it into the diffusion process, enabling precise control over object position and orientation. The appearance maintenance module preserves consistent attributes with a single reference image by employing a three-tiered approach: low-level detail preservation, high-level semantic maintenance, and the integration of 3D priors from a novel view synthesis model. Extensive qualitative and quantitative evaluations on the nuScenes dataset demonstrate DriveEditor’s exceptional fidelity and controllability in generating diverse driving scene edits, as well as its remarkable ability to facilitate downstream tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Altitude control

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No-Reference Image Quality Assessment Based on Active Reasoning Module 6

No-Reference Image Quality Assessment Based on Active Reason...

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6th IEEE International Conference on Electronic information and Communication technology, ICEICT 2023

作者： Qi, Junwei Deng, Yuhao Wang, Qingchun Yang, Zhen Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately. © 2023 IEEE.

关键词： Image quality

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A Cross-direction Task Decoupling Network for Small Logo Detection

arXiv

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arXiv 2023年

作者： Hou, Sujuan Li, Xingzhuo Min, Weiqing Li, Jiacheng Wang, Jing Zheng, Yuanjie Jiang, Shuqiang School of Information Science and Engineering Shandong Normal University Jinan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

关键词： Convolution

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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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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Jing Zhang, Fa Hu, Kai Cui, Xuefeng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665468190

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs). © 2022 IEEE.

关键词： Convolution

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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A comprehensive evaluation method integrating the rough sets with the intuitionistic fuzzy analytic hierarchy process 3

A comprehensive evaluation method integrating the rough sets...

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3rd International Conference on Computer Science and Communication technology, ICCSCT 2022

作者： Wang, Chong Wang, Lei Liu, Bin Cai, Xiangxiang School of Information Engineering Nanchang Institute of Technology Jiangxi Nanchang330000 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Jiangxi Nanchang330000 China

ISBN: (纸本)9781510661240

In this paper, a comprehensive evaluation method integrating the rough sets with the intuitionistic fuzzy analytic hierarchy process (IFAHP) is proposed, in order to overcome the IFAHP not to accurately ascertain the weight of each attribute in terms of real numbers. This comprehensive evaluation method integrating with the advantages of objective and subjective weighting methods, and it makes up for the shortcomings of these two methods to a certain extent. First, the objective weight of each attribute is calculated using the knowledge granulation of the rough sets;second, a new evaluation function is proposed to improve the IFAHP by transferring the intuitionistic fuzzy number obtained into a real one and to obtain the subjective weight of attributes through normalization;third, the subjective and objective weight values are combined by a weighting coefficient to figure out the combined weight of attributes;finally, the values are fused to calculate the integrated evaluation value, thereby obtaining the ranking result. A numerical example shows the feasibility and validity of the ranking result obtained, which has been proved practical to solve the multi-attribute decision-making problems. © 2022 SPIE.

关键词： Analytic hierarchy process

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