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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Sun, Ying Hou, Zhiqiang Yang, Chen Ma, Sugang Fan, Jiulun School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783031476334

A dual-modal feature alignment based object detection algorithm is proposed for the full fusion of visible and infrared image features. First, we propose a two stream detection model. The algorithm supports simultaneous input of visible and infrared image pairs. Secondly, a gated fusion network is designed, consisting of a dual-modal feature alignment module and a feature fusion module. Medium-term fusion is used, which will be used as the middle layer of the dual-stream backbone network. In particular, the dual-mode feature alignment module extracts detailed information of the dual-mode aligned features by computing a multi-scale dual-mode aligned feature vector. The feature fusion module recalibrates the bimodal fused features and then multiplies them with the bimodal aligned features to achieve cross-modal fusion with joint enhancement of the lower and higher level features. We validate the performance of the proposed algorithm using both the publicly available KAIST pedestrian dataset and a self-built GIR dataset. On the KAIST dataset, the algorithm achieves an accuracy of 77.1%, which is 17.3% and 5.6% better than the accuracy of the benchmark algorithm YOLOv5-s for detecting visible and infrared images alone;on the self-built GIR dataset, the detection accuracy is 91%, which is 1.2% and 14.2% better than the benchmark algorithm for detecting visible and infrared images alone respectively. And the speed meets the real time requirements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer 37

DPText-DETR: Towards Better Scene Text Detection with Dynami...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Bo, Du Tao, Dacheng Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China The University of Sydney Australia JD Explore Academy National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781577358800

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://***/ymy-k/DPText-DETR. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Efficiency

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Adaptive group personalization for federated mutual transfer learning 24

Adaptive group personalization for federated mutual transfer...

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Proceedings of the 41st International Conference on Machine Learning

作者： Haoqing Xu Dian Shen Meng Wang Beilun Wang School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a non-parametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines.

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DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing 29

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Pa...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Bin Gao, Miao Fan, Yunlong Sataer, Yikemaiti Gao, Zhiqiang Gui, Yaocheng School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing210096 China School and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China

A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations. However, this work learns graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone (e.g., noisy or incomplete), and (2) graph construction stage and graph representation learning stage are disjoint, the errors introduced in the graph construction stage cannot be corrected and might be accumulated to later stages. To address these two drawbacks, we propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations. Experimental results show that our parser outperforms the previous parsers on the SemEval-2015 Task 18 dataset in three languages (English, Chinese, and Czech). © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Graph neural networks

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Wavelet Convolutional Neural network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory CambridgeMA02139 United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States Texas A&m University Department of Statistics College StationTX77843 United States Texas Tech University Department of Electrical and Computer Engineering LubbockTX79409 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： Convolutional neural networks

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Symmetric Pruning in Quantum Neural networks 11

Symmetric Pruning in Quantum Neural Networks

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Xinbiao Liu, Junyu Liu, Tongliang Luo, Yong Du, Yuxuan Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Pritzker School of Molecular Engineering The University of Chicago United States JD Explore Academy Chicago Quantum Exchange United States Kadanoff Center for Theoretical Physics United States qBraid Co. Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China

Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ansätze generally experience better trainability than those with asymmetric ansätze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ansätze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Hamiltonians

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Constructing and Generating a Time-varying State- Space Vector against Cloud Service Event Interactions 4

Constructing and Generating a Time-varying State- Space Vect...

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2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

作者： Jiang, Yiyi Li, Xiaojian Xia, Chunhe Hu, Hangping Zhang, Zhen Wang, Hailan Guangxi Normal University School of Computer Science Engineering Guilin China Beihang University Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665412704

the abnormal interaction between cloud service events is an essential factor leading to failure. Using neural networks explores the interaction between cloud service events, which needs a method to transform the cloud service events into a vector. However, cloud service events exist as a log with time- varying characteristics. The dataset of cloud service is vast, including numerous strings that cannot be directly involved in calculations. There is a lack of pathway transform data semantics to interaction semantics. Thus, this paper provides a method to construct cloud service logs into vector. Regarding cloud service events as natural language, the method characterizes cloud service events as vector without building a corpus. In addition, we use the semi-supervised neural networks to predict the interaction between cloud service events. The results show that the effectiveness of our method, indicating that it meets the needs of project. This work also provides a foundation for security traceability. © 2021 IEEE.

关键词： Vectors

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基于侧基多通道和对称性破缺效应的分子电导调控

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Science China Materials 2024年第6期67卷 1994-1999页

作者：郝杰汪博宇赵璁霍雅妮王进莹贾传成郭雪峰 Center of Single-Molecule Sciences Institute of Modern OpticsFrontiers Science Center for New Organic MatterTianjin Key Laboratory of Micro-scale Optical Information Science and TechnologyCollege of Electronic Information and Optical EngineeringNankai UniversityTianjin 300350China Network for Computational Nanotechnology School of Electrical and Computer EngineeringPurdue UniversityIndiana 47907USA Beijing National Laboratory for Molecular Sciences National Biomedical Imaging CenterCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不... 详细信息

化学取代是有效调节分子电导,产生多通道输运、量子干涉效应和其他机制的关键方法.但分子骨架和侧基之间存在复杂的相互作用,目前仍未被研究清楚.在本工作中,我们结合扫描隧道显微镜裂结实验技术和第一性原理计算方法,深入研究了具有不同σ和π侧基的分子电导.实验结果及理论分析表明,σ侧基的加入引起了分子前线轨道的对称性破缺并加剧了分子内散射,从而导致电导的降低.相反,π侧基引入了额外的电荷传输通道,实现了电荷传输的多通道效应,增加了分子的电导.这些发现为未来分子功能化和分子电子器件的设计提供了坚实的基础.

关键词： side substituent multiple-channel effect symmetry-breaking effect single-molecule junction

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Balancing Privacy and Performance: A Many-in-One Approach for Image Anonymization 39

Balancing Privacy and Performance: A Many-in-One Approach fo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jia, Xuemei Du, Jiawei Wei, Hui Xue, Ruinian Wang, Zheng Zhu, Hongyuan Chen, Jun National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China A*STAR Singapore A*STAR Singapore

ISBN: (纸本)157735897X

The effective utilization of data through Deep Neural networks (DNNs) has profoundly influenced various aspects of society. The growing demand for high-quality, particularly personalized, data has spurred research efforts to prevent data leakage and protect privacy in recent years. Early privacy-preserving methods primarily relied on instance-wise modifications, such as erasing or obfuscating essential features for de-identification. However, this approach highlights an inherent trade-off: minimal modification offers insufficient privacy protection, while excessive modification significantly degrades task performance. In this paper, we propose a novel Recombining for Obfuscation (FRO) approach to address this trade-off. Unlike existing methods that generate one anonymized instance by perturbing the original data on a one-to-one basis, our FRO approach generates an anonymized instance by reassembling mixed id-related features from multiple original data sources on a many-in-one basis. Instead of introducing additional noise for de-identification, our approach leverages the existing non-polluted features from other instances to anonymize data. Extensive experiments on identity identification tasks demonstrate that FRO outperforms previous state-of-the-art methods, not only in utility performance but also in visual anonymization. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Deep neural networks

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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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