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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guodong Li Letu Qingge Qingyi Pan Pei Yang Department of Computer Technology and Applications Qinghai University Xining China Qinghai Provincial Key Laboratory of Media Integration Technology and Communication Xining China Department of Computer Science North Carolina A&T State University Greensboro NC USA Center for Statistical Science & Department of Industrial Engineering Tsinghua University Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Mural restoration holds significant importance for various reasons from cultural, historical, and artistic aspects. Although significant progress has been made in natural image restoration in recent years, mural image restoration remains a challenging problem due to its differences in structure, color, and style from natural images. Motivated by the structural and color distribution characteristics of mural images, we propose a novel two-stage edge-guided mural in-painting model that perceives global and local information through a Transformer-Multi-scale CNN block (TMC) and learns informative color cues by introducing an extra YUV color space. Extensive experiments on Mural and CelebA-HQ datasets show that our proposed approach outperforms the other state-of-the-art baselines in both qualitative and quantitative evaluations.

关键词： Adaptation models Image color analysis Image edge detection Transformers Image restoration Cultural differences

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Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty

arXiv

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arXiv 2022年

作者： Xu, Wenting Xu, Zhenghua Chen, Junyang Qi, Chang Lukasiewicz, Thomas State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Hebei University of Technology China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science University of Oxford United Kingdom

To reduce doctors’ workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: (i) incomprehensive optimization, (ii) low-order and unidimensional attention mechanisms, and (iii) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards. We also propose a search algorithm with linear complexity to approximate the best weight combination. Furthermore, we use m-linear attention modules to explore high-order feature interactions and to achieve multi-modal reasoning, while a repetition penalty applies penalties to repeated terms during the model’s training process. Extensive experimental studies on two public datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. We also conducted a series of ablation experiments to prove the effectiveness of all our proposed components. We also performed a reward search toy experiment to give evidence that our proposed search approach can significantly reduce the search time while approximating the best performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction 39

Details Enhancement in Unsigned Distance Field Learning for ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Hou, Fei Wang, Wencheng Qin, Hong Zhang, Zhebin He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Advanced Interdisciplinary Sciences University of Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States InnoPeak Technology United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and DCUDF for extracting the local minimal average distance surface. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Surface reconstruction

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Mmaa: An Attribute-Aware Collaborative Learning Network for Radiology Report Generation

SSRN

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SSRN 2024年

作者： Guo, Yuting Li, Shuai Song, Wenfeng Hao, Aimin Qin, Hong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of Computer Science Beijing Information Science and Technology University Beijing100192 China The Department of Computer Science Stony Brook University State University of New York at Stony Brook Stony BrookNY11794-2424 United States

Background and objectives: To date, deep learning-based intelligent diagnosis directly on clinical images has achieved remarkable success. The objective of this research is to leverage artificial intelligence methods to automatically generate intelligent medical reports. This innovation is expected to significantly reduce the workload of senior doctors while enhancing the accuracy of inexperienced radiologists. However, AI-driven automatic approaches still face challenges in generating comprehensive, accurate, and semantically consistent medical reports. One of the challenges is that the generated text descriptions, although mimicking the writing and structural styles of clinical reports, often fail to precisely identify diagnostic attributes and their semantics. Another challenge is the difficulty in capturing patient-specific symptoms due to the imbalanced distribution of training ***: This paper proposes an intelligent medical report generation system that employs collaborative learning of image features and clinical symptom attributes. This approach aims to effectively combine spatial visual features extracted from clinical images with underlying personalized symptom attributes. The system includes an encoder that learns spatial features from clinical images. To incorporate medical knowledge of symptoms, attribute labels are embedded as bottom-up features and fused with the encoded spatial features (acting as top-down features). Our novel feature fusion strategy involves two important aligning modules: the image-attribute memory and the text-attribute memory. These modules are designed to capture the relationships among attributes and their distributions, as well as guide the attention of the decoder during report ***: Extensive experiments and comprehensive evaluations on two public X-ray image datasets validate the effectiveness and advantages of our novel approach. Especially, the BLEU-2 metric increased by 1.8% on the IU-Xray datas

关键词： Semantics

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MIntRec: A New Dataset for Multimodal Intent Recognition

arXiv

引用

arXiv 2022年

作者： Zhang, Hanlei Xu, Hua Wang, Xin Zhou, Qianrui Zhao, Shaojie Teng, Jiayan State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University School of Information Science and Engineering Hebei University of Science and Technology China

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. Copyright © 2022, The Authors. All rights reserved.

关键词： Character recognition

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Joint Optimization of Dynamic Service Selection and Request Routing in Cloud-Edge Collaborative Environments

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IEEE Internet of Things Journal 2025年

作者： Tang, Bing Xu, Wei Zhang, Li Cao, Buqing Yang, Qing Li, Kuanching Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Computer Science Guangzhou510725 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Hunan Institute of Engineering School of Information Science and Engineering Xiangtan411104 China Hunan University of Technology School of Computer Zhuzhou412007 China Hunan University of Science and Technology Sanya Research Institute Sanya572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms. © 2014 IEEE.

关键词： Edge computing

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Learning Self-Target Knowledge for Few-Shot Segmentation

SSRN

引用

SSRN 2023年

作者： Chen, Yadang Chen, Sihan Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China State Key Laboratory of Computer Science Institute of Software University of Chinese Academy of Sciences Beijing100190 China

Few-shot semantic segmentation uses a few annotated data of a specific class in the support set to segment the target of the same class in the query set. Most existing approaches fail to perform well when there are significant intra-class variances. This paper alleviates the problem by concentrating on mining the query image and using the support set as supplementary information. First, it proposes a Query Prototype Generation Module to generate a query foreground prototype from the query features. Specifically, we use both prototype-level and pixel-level similarity matching to generate two complementary initial prototypes, which we then integrate to create a discriminative query foreground prototype. Second, we propose a Support Auxiliary Refinement Module to further guide the final precise prediction of the query image by leveraging the target category information of the support set through step-by-step mining. Specifically, we generate a query-support mixture prototype based on the support prototype representation obtained using the attention mechanism. Then we generate a support supplement prototype to complement the missing information by encoding over the foreground regions that the query-support mixture prototype fails to segment out. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works of few-shot segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image

PREIM3D: 3D Consistent Precise Image Attribute Editing from ...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jianhui Li Jianmin Li Haoji Zhang Shilong Liu Zhengyi Wang Zihao Xiao Kaiwen Zheng Jun Zhu Department of Computer Science and Technology Institute for AI BNRist Tsinghua University State Key Laboratory of Astronautic dynamics Xi'an Satellite Control Center RealAI

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view, they still suffer from the 3D inconsistency of produced images at large camera poses and imprecise image attribute editing, like affecting unspecified attributes during editing. For more efficient image inversion, we train a shared encoder for all images. To alleviate 3D inconsistency at large camera poses, we propose two novel methods, an alternating training scheme and a multi-view identity loss, to maintain 3D consistency and subject identity. As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images. We compare the latent space and inversion manifold of GAN models and demonstrate that editing in the inversion manifold can achieve better results in both quantitative and qualitative evaluations. Extensive experiments show that our method produces more 3D consistent images and achieves more precise image editing than previous work. Source code and pretrained models can be found on our project page: https://***/Preim3D/.

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DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross Domain

arXiv

引用

arXiv 2024年

作者： Liu, Jun Zhou, Jiantao Zeng, Jiandian Tian, Jinyu Li, Zheng State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Institute of Artificial Intelligence and Future Networks Beijing Normal University China School of Computer Science and Engineering Macau University of Science and Technology China

This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on a hierarchical DIsentangled Feature space, called DifAttack++, which differs significantly from the existing ones operating over the entire feature space. Specifically, DifAttack++ firstly disentangles an image’s latent feature into an Adversarial Feature (AF) and a Visual Feature (VF) via an autoencoder equipped with our specially designed Hierarchical Decouple-Fusion (HDF) module, where the AF dominates the adversarial capability of an image, while the VF largely determines its visual appearance. We train such two autoencoders for the clean and adversarial image domains (i.e., cross-domain) respectively to achieve image reconstructions and feature disentanglement, by using pairs of clean images and their Adversarial Examples (AEs) generated from available surrogate models via white-box attack methods. Eventually, in the black-box attack stage, DifAttack++ iteratively optimizes the AF according to the query feedback from the victim model until a successful AE is generated, while keeping the VF unaltered. Extensive experimental results demonstrate that our DifAttack++ leads to superior ASR and query efficiency than state-of-the-art methods, meanwhile exhibiting much better visual quality of AEs. The code is available at https://***/csjunjun/***. © 2024, CC BY-NC-ND.

关键词： Codes (symbols)

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