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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Fast Minimum Spanning Tree Clustering Algorithm Based on Granular-Ball Computing 11th

Fast Minimum Spanning Tree Clustering Algorithm Based on Gr...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Jiang, Xiaocui Cheng, Dongdong Huang, Jinlong Zhang, Sulan Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 400065 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing 408100 China

ISBN: (纸本)9783031927461

Distance measure is a key issue in clustering analysis. Most existing clustering algorithms, like K-means and density peaks clustering, use Euclidean distance to measure the dissimilarity between objects, making it difficult for discovering irregular-shaped clusters. Some algorithms specially designed for recognizing irregular-shaped clusters have high time complexity. In this paper, we introduce granular-ball (GB) into clustering and use the neighbors of the boundary objects of granular-balls to quantitatively evaluate the distance between granular-balls. By combining minimum spanning tree (MST)-based clustering algorithm, we propose a GB-based MST clustering algorithm, called GB-MST. Since GB model is a multi-granularity data representation and the number of GBs is far less than that of objects in a dataset, the proposed algorithm greatly reduces the running time of MST-based clustering algorithms and the effective distance between GBs makes it suitable for discovering irregular-shaped clusters. The experiment results on synthetic and real datasets have shown that GB-MST outperforms the compared algorithms in terms of time and effectiveness in discovering irregular-shaped clusters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： clustering granular-ball minimum spanning tree quantitative distance measure

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Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

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Journal of Mountain Science 2024年第3期21卷 952-972页

作者： GUO Junqi XI Wenfei YANG Zhiquan SHI Zhengtao HUANG Guangcai YANG Zhengrong YANG Dongqing Faculty of Geography Yunnan Normal UniversityKunming 650500China Key Laboratory of Early Rapid Identification Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan ProvinceKunming 650093China Faculty of Public Safety and Emergency Management Kunming University of Science and TechnologyKunming 650093China Key Laboratory of Geological Disaster Risk Prevention and Control and Emergency Disaster Reduction of Ministry of Emergency Management of the People’s Republic of China Kunming University of Science and TechnologyKunming 650093China Guizhou Geological Survey Institute Guiyang 550081China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and *** order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network *** SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and *** Reservoir area is selected as a case study for *** indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard ***,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,***,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation *** area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation ra

关键词： Baihetan SBAS-InSAR SSA-BP Landslide hazard susceptibility evaluation

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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EncryptFlow: Efficient and Lossless Image Encryption Network Based on Normalizing Flows

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Yang, Menglin Xie, Dong Zhang, Guiting Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System Wuhu241002 China Anhui Normal University School of Computer and Information Wuhu241002 China

Compared to cryptographic image encryption schemes, neural networks (NN) based image encryption schemes exhibit a significantly larger key space and offer enhanced capabilities for parallel processing of image data. However, most existing NN-based image encryption schemes suffer from high time complexity in generating random keys, and their decryption processes often fail to fully recover the plaintext images without loss. In this paper, we first propose a normalizing flows based encryption network, called EncryptFlow, designed to achieve efficient and lossless image encryption. Normalizing flows employ a special coupling structure to couple the partitioned data, thereby establishing interdependence among them. Specifically, we utilize coupling structures (e.g. additive coupling) that allows the image blocks to alternately encrypt each other during forward propagation. Additionally, we devise a key generation algorithm that produces sub-keys tailored for each layer of the encryption network. The proposed EncryptFlow network seamlessly integrates both encryption and decryption functionalities, leveraging the XOR operation as the encryption function within each layer. The experimental results and comparative analyses indicate that EncryptFlow can encrypt 256 × 256 grayscale images with an average time of merely 0.047s, and similarly, it requires only 0.188s to encrypt color images of the same dimensions. © 2020 IEEE.

关键词： Optical flows

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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Low-light Image Enhancement through Learning a Simplified Inverse Rendering Model

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wu, Wenhui Pang, Jia Gao, Shuaibo Zhang, Pingping Chen, Zhe Qiu, Guoping Shenzhen University College of Electronics and Information Engineering China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China City University of Hong Kong Department of Computer Science Hong Kong University of Nottingham School of Computer Science United Kingdom

It remains to be extremely difficult to capture high quality photographs of low-light scenes. Low light causes the low signal-to-noise ratio (SNR) problem which makes the image noisy. Such scenes almost always have the high dynamic range (HDR) problem caused by uneven lighting where a small area surrounding the light source is very bright while the rest of the scene is very dark, making it very difficult to simultaneously obtain high quality signals in both the dark and bright areas. This paper presents a new image restoration method for tackling the problems in low-light scenes. Fundamentally differing from existing approaches, the new method borrows ideas from inverse graphics rendering and re-renders the image with a canonical light source thus correcting the image from first principle. A deep learning based simplified inverse rendering model (SIRM) featuring implicit regularization is first developed for correcting uneven lighting and then an end-to-end convolutional neural network is constructed for reducing noise. Extensive experimental results are presented to demonstrate that the new method outperforms state-of-the-art methods, and is capable of effectively brightening up dark image regions while at the same time preserving details and color consistency. Our code is available at: https://***/pj0927/SIRNet. © 2025 IEEE.

关键词： Rendering (computer graphics)

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module

Polyp Segmentation Neural Network with Simple Encoding Struc...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Jie Wang Zuoyong Li Haiping Xu Xuesong Cheng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation.

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