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arXiv 2024年

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction

arXiv

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arXiv 2022年

作者： Cao, Tianwei Xu, Qianqian Yang, Zhiyong Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction. © 2022, CC0.

关键词： Recommender systems

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A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

arXiv

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arXiv 2022年

作者： Wang, Huadeng Liu, Zhipeng Lan, Rushi Liu, Zhenbing Luo, Xiaonan Pan, Xipeng Li, Bingbing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://***/antifen/mitosis-nuclei-segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep

arXiv

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arXiv 2024年

作者： Wu, Jingqian Zhu, Shuo Wang, Chutian Shi, Boxin Lam, Edmund Y. Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Hong Kong National Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China

—Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficient to capture, especially in dynamic environments. Event cameras, with their high temporal resolution and ability to capture asynchronous brightness changes, offer a promising alternative for more reliable scene reconstruction without motion blur. In this paper, we propose SweepEvGS, a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis across various imaging settings from a single sweep. SweepEvGS utilizes the initial static frame with dense event streams captured during a single camera sweep to effectively reconstruct detailed scene views. We also introduce different real-world hardware imaging systems for real-world data collection and evaluation for future research. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings: synthetic objects, real-world macro-level, and real-world micro-level view synthesis. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency, highlighting its potential for dynamic practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints

Joint Sparsification and Quantization for Wireless Federated...

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IEEE Workshop on Signal processing Advances in Wireless Communications (SPAWC)

作者： Junshen Su Xijun Wang Xiang Chen Ray-Guang Cheng School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.

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Symptom and Pathology Report Generation for Ophthalmic Diseases in Fundus Images

Symptom and Pathology Report Generation for Ophthalmic Disea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Wen Leiting Chen Lifeng Qiao Yu Deng Siying Dai Junjing Chen Chuan Zhou Key Laboratory of Digital Media Technology of Sichuan Province School of Computer Science and Engineering Institute of Electronic and Information Engineering Guangdong School of Medicine University of Electronic Science and Technology of China Sichuan Provincial People’s Hospital King’s College London London UK

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

While fundus image interpretation and report writing are routine procedures in the diagnosis of ophthalmic diseases, they are error-prone for inexperienced ophthalmologists and laborious and tedious for experienced ophthalmologists. Therefore, there is an urgent need for an automated ophthalmic report generation tool. Existing methods for captioning medical images are confined to the field of radiological images, and there is no such method for fundus images. To address this issue, we studied automated report generation for fundus images. Unlike the radiological field, this task presents several challenges. First, there are a wide variety of ophthalmic diseases, and identifying diseases in fundus images is sometimes difficult since the diseases can have different manifestations. Second, an ophthalmic disease may have multiple subtypes, which can be distinguished from one another only through accurate identification o f specific ophthalmic symptoms. To overcome these challenges, we propose a method with the following features: (1) a multitask learning framework for predicting both ophthalmic symptoms and pathologies and for generating paragraphs, (2) two-branch architecture for handling ophthalmic symptoms and pathologies separately, and (3) a causal linkage module for transferring the symptom information for accurately predicting ophthalmic pathologies and thereby facilitating the identification of subtypes. We demonstrate the effectiveness of the proposed method for an in-house fundus image data set.

关键词： Pathology Diseases Biomedical imaging Retina Medical diagnostic imaging Hemorrhaging Visualization

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection

Enhancing Online Teaching Effectiveness Through Hand-Raising...

引用

information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Zhiwei Dong Xiaopan Chen Yufeng Shi Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments.

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The Novel Sensor Network Structure for Classification processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

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PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model

arXiv

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arXiv 2024年

作者： Wang, Yuqing Huang, Zhongling Yang, Shuxin Tang, Hao Qiu, Xiaolan Han, Junwei Zhang, Dingwen Brain and Artificial Intelligence Laboratory School of Automation Northwestern Polytechnical University Xi’an710072 China the National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China the National Key Laboratory of Microwave Imaging Technology Chinese Academy of Sciences Beijing100090 China the Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used. However, these formats often face issues related to usability, interpretability, and data integrity. While most feature extraction networks for PolSAR attempt to address these issues, they are typically small in size, which limits their ability to effectively extract features. To overcome these limitations, we introduce the large, powerful Segment Anything Model (SAM), which excels in feature extraction and prompt-based segmentation. However, SAM’s application to PolSAR is hindered by modality differences and limited integration of domain-specific knowledge. To address these challenges, we propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), which incorporates physical scattering characteristics and a novel prompt generation strategy to enhance segmentation performance with high data efficiency. Our approach includes a new data processing pipeline that utilizes polarimetric decomposition and semantic correlations to generate Microwave Vision Data (MVD) products, which are lightweight, physically interpretable, and information-dense. We extend the basic SAM architecture with two key contributions: The Feature-Level Fusion Prompt (FFP) module merges visual tokens from the pseudo-colored SAR image and its associated MVD, enriching them with supplementary information. When combined with a dedicated adapter, it addresses modality incompatibility in the frozen SAM encoder. Additionally, we propose the Semantic-Level Fusion Prompt (SFP) module, a progressive mechanism that leverages semantic information within MVD to generate sparse and dense prompt embeddings, refining segmentation details. Experimental evaluation on the newly constructed PhySAR-Seg datasets shows that PolSAM outperforms existing SAM-based models (without MVD) and other multimodal fusion

关键词： Polarimeters

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DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection

arXiv

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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

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