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作者： Lin, Yuqing Lin, Jiawen Niu, Yuzhen Zhang, Haifeng College of Mathematics and Computer Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Xueyuan Road #2 Fuzhou Fujian350106 China

With the diversified development of the digital devices, such as computer, mobile phone and television, how to resize an image or video to adapt to different display screens has been a heated topic. Seam carving does well in image resizing at most times, however, it sometimes produces discontinuity image content or impaired salient objects. Therefore, we propose an accumulative energy-based seam carving method for image resizing. We distribute the energy of each pixel on the seam to its adjacent 8-connected pixels to avoid the extreme concentration of seams. In addition, we add the image saliency and the edge information into the energy function to reduce the distortion. To compute more efficiently, we use parallel computing environment as well. Experimental results show that compared with the existing methods, our method can both avoid the discontinuity of image content and distortions as well as better maintain the shape of the salient objects. © 2020 Inderscience Enterprises Ltd.

关键词： Digital devices

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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DMU-Net: a dual stream multi-scale U-Net for image splicing forgery localization

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Machine Vision and Applications 2025年第4期36卷

作者： Yu, Niankang Su, Lichao Wang, Jinli Huang, Liming College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Wulongjiangbei Avenue Fuzhou350116 China

With advancements in image processing and the proliferation of editing software, image splicing forgery has become increasingly facile to execute yet harder to detect, thereby impacting societal security. Effective detection and localization methods are urgently needed. Existing methods, while somewhat effective, often over-rely on semantic features, overlook shallow features, and struggle to adapt to varying tampered region sizes. To address these issues, we propose a two-stream image splicing forgery localization network, named DMU-Net. The network first introduces a noise stream as a supplementary feature alongside the RGB stream to provide a richer feature representation. Subsequently, we improve the Atrous Spatial Pyramid Pooling module by incorporating an attention mechanism that enables the model to obtain feature maps of different scales, effectively use context information, and enhance the ability to capture tampered regions of various sizes. Finally, we employ a dual attention mechanism to fuse features from both the encoder and decoder stages;this approach effectively leverages shallow features for fusing coarse-grained and fine-grained features, thus enhancing the model’s ability to capture features across different dimensions. Extensive experimental results show that the proposed method outperforms the state-of-the-art image splicing localization methods in terms of detection accuracy and robustness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Photointerpretation

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A Graph-Convolutional-network based Prototype Mixing Model for Few-shot Segmentation 11

A Graph-Convolutional-Network based Prototype Mixing Model f...

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11th International Conference on information Technology in Medicine and Education, ITME 2021

作者： Gu, Zhibo Luo, Zhiming Huang, Min Cai, Yuanzheng Li, Shaozi Xiamen University Department of Artificial Intelligence Xiamen China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China College of Humanities Xiamen University Xiamen China

ISBN: (纸本)9781665406796

Over the past few years, deep convolutional neural networks (CNNs) based semantic segmentation methods reached the state-of-the-art performance. To train a model with the ability to know a concept, a lot of pixel level annotated images are required, which is time consuming and hard to cover unseen object categories. Thus, few-shot semantic segmentation has been developed to implement segmentation with a few annotation images. In this paper, we proposed a novel prototype mixing model for few shot segmentation. Different with other works which only produce prototypes form support set, our proposed model learn a group of concept-specific prototypes from support set and then generate prototypes from query set. With prototypes from both query set and support set, we proposed a GCN(Graphic Convolutional network) module to generate mixing prototypes for better utilizing of informations from different categories. We also proposed a clustering module to produce multi-prototypes for representing different parts of a single semantic class, which reach better performance than single prototype. Our model achieve 48.8% and 55.9%mIoU score on PASCAL-5i for 1-shot and 5-shot settings respectively. © 2021 IEEE.

关键词： Semantic Segmentation

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Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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Jisuanji Xuebao/Chinese Journal of Computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

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A Deep Reinforcement Learning Framework for Vehicle Detection and Pose Estimation in 3D Point Clouds 6th

A Deep Reinforcement Learning Framework for Vehicle Detectio...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Wang, Weipeng Luo, Huan Zheng, Quan Wang, Cheng Guo, Wenzhong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350003 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University XiamenFJ361005 China

ISBN: (纸本)9783030578800

As for autonomous driving in urban environments, it is of significance to accurately capture the position and pose of vehicles. Those information can assist self-driving system in making right decisions to avoid potential risks. Currently, 3D point clouds captured by laser scanners are widely used in self-driving systems to sense the real environment. Therefore, in this paper, we propose a deep reinforcement learning framework for vehicle detection and pose estimation by using 3D point clouds. Specifically, to estimate the pose of vehicles, we propose to design a rotation action in Deep Q network (DQN). In addition, by considering the whole detection procedure as Markov Decision Process (MDP), our intermediate detected results can further improve the detection performance of our proposed method. The evaluations are carried on outdoor point cloud scenes captured by VMX450 laser scanning system. The experimental results demonstrate the satisfied performance on vehicle detection and pose estimation. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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RSFDM-NET: REAL-TIME SPATIAL AND FREQUENCY DOMAINS MODULATION network FOR UNDERWATER IMAGE ENHANCEMENT

arXiv

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arXiv 2023年

作者： Jiang, Jingxia Bai, Jinbin Liu, Yun Yin, Junjie Chen, Sixiang Ye, Tian Chen, Erkang School of Ocean Information Engineering Jimei University Xiamen China Department of Computer Science National University of Singapore Singapore College of Artificial Intelligence Southwest University Chongqing China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Specifically, our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying low-frequency background information and high-frequency detail features, which effectively promote the network to model global background information and local texture details. To more precisely correct the color cast and low saturation of the image, we introduce a Three-branch Feature Extraction (TFE) block in the primary net that processes images pixel by pixel to integrate the color information extended by the same channel (R, G, or B). This block consists of three small branches, each of which has its own weights. Extensive experiments demonstrate that our network significantly outperforms over state-of-the-art methods in both visual quality and quantitative metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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RSFDM-Net: Real-Time Spatial and Frequency Domains Modulation network for Underwater Image Enhancement

RSFDM-Net: Real-Time Spatial and Frequency Domains Modulatio...

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IEEE International Conference on Image processing

作者： Jingxia Jiang Jinbin Bai Yun Liu Junjie Yin Sixiang Chen Tian Ye Erkang Chen School of Ocean Information Engineering Jimei University Xiamen China Department of Computer Science National University of Singapore Singapore College of Artificial Intelligence Southwest University Chongqing China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

关键词：

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CenterPicker:An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection

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Journal of Cyber Security 2022年第2期4卷 65-77页

作者： Jianquan Ouyang Jinling Wang Yaowu Wang Tianming Liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan Province411100China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and *** must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM ***,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D *** propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy *** method uses a fully convolutional neural network to generate a keypoint *** heatmap value represents the probability that a micrograph pixel belongs to a particle center *** can process images of any size and can directly predict the center point and size of the *** network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection *** have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.

关键词： Cryo-electron microscope deep learning particle picking object detection

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