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arXiv 2023年

作者： Chen, Qiong Huang, Tianlin Zhu, Geren Lin, Enlu School of Computer Science and Engineering South China University of Technology China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods. © 2023, CC BY-NC-SA.

关键词： Learning systems

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User-Specified Conditional Preferences Driven Personalized Skyline Web Services Selection

User-Specified Conditional Preferences Driven Personalized S...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Senfu Ke Xiaodong Fu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan Province China Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan Province China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

The proliferation of web services with similar functionalities challenges selecting the most relevant service based on Quality of Service (QoS). Skyline queries help by reducing the candidate pool, but the resulting service skyline can still be extensive, making selection difficult. A personalized Skyline service selection method provides a smaller, more manageable set. Current approaches often overlook inter-dependencies between user preferences, complicating complex preference management. This paper introduces CP-Skyline, an efficient method leveraging Conditional Preference Networks (CP-Nets) to incorporate user-specific preferences and personalize the service selection process. We present a personalized conditional preference model based on CP-Nets to prune candidate services and compress the query space, defining a new dominance relation for CP-Skyline computation, and validate our approach through extensive experiments on synthetic and real-world datasets.

关键词： Web services Computational modeling Quality of service Software engineering

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing 41

How Universal Polynomial Bases Enhance Spectral Graph Neural...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. Copyright 2024 by the author(s)

关键词： Wiener filtering

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Three-Dimensional Spatial-Temporal Near-Field Passive Localization Based on an Exact Spatial Propagation Model

Three-Dimensional Spatial-Temporal Near-Field Passive Locali...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaxiong Fang Juan Liu Hua Chen Wei Liu Ye Tian Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image

SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiatai Lin Guoqiang Han Jingxuan Zhou Zhenwei Shi Zaiyi Liu Chu Han South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Semantic segmentation for histopathology images is one of the fundamental tasks in computational pathology. Due to the high cost of pixel-level annotation acquisition, the weakly supervised semantic segmentation (WSSS) attempts to achieve information-intensive segmentation task for histopathology images to reduce the labeling effort of pathologists by leveraging image-level labels. However, traditional WSSS requires a large-scale training set with image-level labels, which still imposes considerable labeling costs on pathologists. To this end, this work proposes a Small-Scale Weakly Supervised Semantic Segmentation (SS-WSSS) approach to achieve the comparable performance only with small-scale weakly-labeled data to further reduce pathologist’s labeling effort. Since histopathology images can easily generate massive unlabeled data, our SS-WSSS aims to learn with the unlabeled data to bridge the information gap. First, we propose a Single-to-Multi Prototype Similarity (S2M-PS) method to generate reliable pseudo-labels for unlabeled data by measuring the similarity between single-label prototypes and multi-label feature maps. Then, we introduce a Cross-Task CoTraining (CT 2 ) method for pseudo-supervision of models with pseudo-labels self-refinement to avoid overfitting to noisy labels. We conduct the experiment on two public datasets to demonstrate the effectiveness of our SS-WSSS. In the experiment, our method achieves comparable performance with SOTA methods only using 30% labeled data.

关键词： Training Costs Histopathology Semantic segmentation Semantics Pipelines Prototypes Labeling Noise measurement Reliability

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Design of P Band Filter Antenna with High Out-of-Band Suppression

Design of P Band Filter Antenna with High Out-of-Band Suppre...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Yinsu Yuan Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

P-band antennas offer tremendous potential in various fields, including remote sensing, communication, and radar applications. However, It is crucial to enhance their effectiveness in these domains that the current challenges in the P-band, particularly wide bandwidth and exceptional out-of-band suppression be achieved. This paper aims to introduce an efficient design scheme for a high-efficiency, high out-of-band suppression P-band filter antenna. The antenna structure comprises a Y-shaped feeding network, upper and lower parallel dielectric plates, a vertical dielectric plate, a central metal plate, and a pair of microstrip patches. The bottom feeding network employs a Y-shaped feeding network. The coupling of the antenna was effectively controlled and the good bandwidth and impedance matching have been achieved. In order to achieve a wider bandwidth and lower return loss for the antenna, slotting technology was employed on the semi-circular radiation patch. The introduction of a vertical metal plate enhances filtering performance of antenna. Additionally, there are radiation zeros at 0.68 GHz and 1.43 GHz, high-frequency selectivity is imparted and signal reflection losses are effectively reduced. key performance indicators include a filter antenna center frequency of 0.75 GHz, a −10 dB impedance bandwidth of 32.9% (0.89 GHz–1.24 GHz). In the upper stopband (0.5 GHz–0.8 GHz), the suppression level exceeds 20 dB, while in the lower stopband (1.1 GHz–1.8 GHz), suppression level exceeds 15 dB. Significant suppression characteristics are observed in the 0.5 GHz–0.8 GHz and 1.1 GHz–1.8 GHz frequency ranges. Design elements such as the semi-circular radiating patch, slotting technology, and rectangular slots on the substrate have been employed in the antenna. The suppression level, frequency selectivity had been enhance of antenna. Simulation results indicate a gain of 7.06 dB, overall efficiency beyond 95%, co-pol superior to cross-pol by more than 35 dB, and antenna

关键词： Slot antennas Filtering Simulation Bandwidth Microstrip antennas Directive antennas Dielectrics

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

arXiv

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arXiv 2023年

作者： Yang, Yong Chen, Qiong Feng, Yuan Huang, Tianlin School of Computer Science and Engineering South China University of Technology China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet. © 2023, CC BY-NC-SA.

关键词： Embeddings

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GLFAFormer: DeepFake forgery detection with adaptive feature extract and align

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Digital Signal Processing: A Review Journal 2025年 165卷

作者： Chen, Long Zhao, Huihuang He, Jiaxin Meng, Weiliang College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With the rapid development of generative models, there is an increasing demand for universal fake image detectors. In this paper, we investigate the problem of fake image detection for the synthesis of generative models to detect fake images from multiple generative methods. Recent research methods explore the benefits of pre-trained models and mainly adopt a fixed paradigm of training additional classifiers separately, but we find that the fixed paradigm hinders the full learning of forgery features, leading to insufficient representation learning in the detector. The main reason is that the fixed paradigm pays too much attention to global features and neglects local features, which limits the ability of the model to perceive image details and leads to some information loss or confusion. In this regard, based on the pre-trained visual language space, our method introduces two core designs. First, we designed a Deep Window Triple Attention (DWTA) module. A similar dense sliding window strategy is adopted to capture multi-scale local abnormal patterns, and the sensitivity to generated artifacts is enhanced through the triple attention mechanism. Secondly, we proposed a Cross-Space Feature Alignment (CSFA) module. A two-way interactive channel between global features and local features is established, and the alignment loss function is used to achieve semantic alignment of cross-modal feature spaces. The aligned features are adaptively fused through a gating mechanism to obtain the final adaptive forged features. Experiments demonstrate that our method, when trained solely on ProGAN data, achieves superior cross-generator generalization: it attains an average accuracy of 94.7% on unseen GANs and generalizes to unseen diffusion models with 94% accuracy, surpassing existing methods by 2.1%. The source code is available at https://***/long2580h/GLFAFormer. © 2025 Elsevier Inc.

关键词： Visual languages

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Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis

arXiv

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arXiv 2025年

作者： Wu, Wenyi Zhang, Hao Wei, Zhisen Jing, Xiao-Yuan Zhang, Qinghua Wu, Songsong Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis School of Computer Guangdong University of Petrochemical Technology Maoming525000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Province Zhangzhou363000 China

Source-free domain adaptation (SFDA) has been exploited for cross-domain bearing fault diagnosis without access to source data. Current methods select partial target samples with reliable pseudo-labels for model adaptation, which is sub-optimal due to the ignored target samples. We argue that every target sample can contribute to model adaptation, and accordingly propose in this paper a novel SFDA-based approach for bearing fault diagnosis that exploits both reliable and unreliable pseudo-labels. We develop a data-augmentation-based label voting strategy to divide the target samples into reliable and unreliable ones. We propose to explore the underlying relation between feature space and label space by using the reliable pseudo-labels as ground-truth labels, meanwhile, alleviating negative transfer by maximizing the entropy of the unreliable pseudo-labels. The proposed method achieves well-balance between discriminability and diversity by taking advantage of reliable and unreliable pseudo-labels. Extensive experiments are conducted on two bearing fault benchmarks, demonstrating that our approach achieves significant performance improvements against existing SFDA-based bearing fault diagnosis methods. Our code is available at https://***/BdLab405/SDALR Copyright © 2025, The Authors. All rights reserved.

关键词： Failure analysis

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Evolution of AVS video coding standards: twenty years of innovation and development

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Science China(Information Sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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