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SSRN 2024年

作者： Zhang, Dengyong Jiang, Ningjing Li, Feng Chen, Jiaxin Liao, Xin Yang, Gaobo Ding, Xiangling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer and Communication Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

Image splicing can easily be used in illegal activities, which may have negative impact on society. Therefore, detecting splicing images and precisely localizing tampered regions are challenging tasks. In this work, we propose a dual-branch Multi-Scale Noise-Guided Progressive Network (MSNP-Net). The multi-resolution branch extracts deep semantic features of images while suppressing redundant noise. A multi-scale noise-guided branch is designed to capture more subtle tampering artifacts and guide the network to strengthen the learning of spatial structure features. The two branches complement and restrict each other. The features of different scales are fused in a gradual mechanism, and the feature expression is aggregated through the spatial channel feature aggregation module (SCAM). A large number of experiments show that the MSNP-Net proposed in this paper has better completeness and lower false alarm rate for the detection and localization of spliced images. It can achieve more refined detection results, and has good stability and robustness. Significantly outperforms other state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

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Computers, Materials & Continua 2019年第2期58卷 493-504页

作者： Min Long Yan Zeng Changsha University of Science and Technology Changsha410014China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410014China

Aim to countermeasure the presentation attack for iris recognition system,an iris liveness detection scheme based on batch normalized convolutional neural network(BNCNN)is proposed to improve the reliability of the iris authentication *** BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris,including convolutional layer,batch-normalized(BN)layer,Relu layer,pooling layer and full connected *** iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels,and then the iris features are extracted by *** these features,the genuine iris and fake iris are determined by the decision-making *** normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during *** experiments are conducted on three classical databases:the CASIA Iris Lamp database,the CASIA Iris Syn database and Ndcontact *** results show that the proposed method can effectively extract micro texture features of the iris,and achieve higher detection accuracy compared with some typical iris liveness detection methods.

关键词： Iris liveness detection batch normalization convolutional neural network biometric feature recognition

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CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction 39

CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： An, Yajun Chen, Jiale Lin, huan Liu, Zhenbing Feng, Siyang Zhang, Hualong Lan, Rushi Liu, Zaiyi Pan, Xipeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing 402160 China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin 541004 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou 510080 China

ISBN: (纸本)157735897X

Cancer is a leading cause of death worldwide due to its aggressive nature and complex variability. Accurate prognosis is therefore challenging but essential for guiding personalized treatment and follow-up. Previous research often relied on single data sources, missing the opportunity to combine various types of patient information for more comprehensive survival predictions. To address these challenges, we propose a two-stage fusion method named Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework (CA-MLIF). In the first stage, we propose a CA mechanism for real-time feature updates and cross-modal mutual learning to capture rich semantic information. In the second stage, we design a novel multimodal low-rank interaction fusion method for survival prediction. Specifically, we present modal attention mechanism (MAM) for feature filtration, low-rank multimodal fusion (LMF) for model complexity reduction, and optimal weight concatenation (OWC) for maximizing feature integration. Extensive experiments on two public datasets TCGA-GBMLGG and TCGA-KIRC, as well as a multi-center in-house lung adenocarcinoma (LUAD) dataset validate the effectiveness of CA-MLIF, which demonstrate that our method outperforms existing approaches in survival prediction under both pathology-gene fusion and CT-pathology fusion scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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RSSD: Object Detection via Attention Regions in SSD Detector 2

RSSD: Object Detection via Attention Regions in SSD Detector

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2nd International Conference on Safety Produce Informatization, IICSPI 2019

作者： Zhou, Shuren Qiu, Jia Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China

ISBN: (纸本)9781728145662

This paper designs a module of attention regions in SSD detector for accurate and efficient object detection (RSSD). Different from previous one-stage detection method like SSD which just simply applied the multi-scale head-features and directly extracted from backbone network, for classification and regression, our method aims to strengthen the characterization of head-features further. The parallel encode-to-decode structure is constructed and a computation method of regional distribution on features (R-Softmax) is proposed. What's more, in order to reduce time-costs, the down-sampling layers are shared with the multi-scale layers from backbone network. Our detector performs better on PASCAL VOC datasets (e.g., 78.4% mAP V.S. SSD 76.4% on VOC 07test) and costs 0.001s per image more than SSD. © 2019 IEEE.

关键词： Object detection

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PROM: A persistent routing optimization method based on supervised learning

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Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo huang Mingjie Ding Bin Lin huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic Engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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Instant Edit Propagation on Images Based on Bilateral Grid

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Computers, Materials & Continua 2019年第8期61卷 643-656页

作者： Feng Li Chaofeng Ou Yan Gui Lingyun Xiang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore639798Singapore

The ability to quickly and intuitively edit digital content has become increasingly important in our everyday ***,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large ***,existing edit propagation methods are generally inefficient and highly ***,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image ***,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral *** a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of *** then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis *** results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.

关键词： Instant edit propagation bilateral grid radial basis function image editing

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A survey of multi-object video tracking algorithms

A survey of multi-object video tracking algorithms

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Qiu, Jia Wang, Jin Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Video multi-object tracking is one of the important research topics in the field of computer vision, which is widely used in military and civil areas. At present, the research of single object tracking algorithm is quite mature, however the research of multi-object tracking is still ongoing. This paper focuses on four important stages in the multi-object tracking process: feature extraction, detector, data association and the tracker. The feature extraction part introduces the current methods of feature extraction, as well as the merits and demerits of each method;In the stage of detection, the tracking effect of the object appearance model in specific applications is described, and then the paper analyze the multi-object tracking algorithm based on detection and tracking as well as the multi-object tracking algorithm based on deep learning;In the tracking stage, the establishment of object motion model and multi-object tracking with different tracker hybrid algorithm are introduced;During the stage of data association, the paper introduce the multi-object tracking based on energy minimization and commonly used data association algorithm, respectively. Then the current mainstream datasets and evaluation methods are introduced. Finally, the future development of the multi-object tracking is discussed and forecasted. © Springer Nature Switzerland AG 2019.

关键词： Feature extraction

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An overview of image-based person re-identification

An overview of image-based person re-identification

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Luo, Peng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. In this paper, the image based traditional person re-ID is introduced from feature description and metric learning. In addition, with the extensive application of the deep learning algorithm in recent years, it also brings about the change of person re-ID algorithm. From the three aspects of loss function design, local feature and data augmentation, this paper introduces some work of person re-ID algorithm based on deep learning. Finally, it summarizes the development of pedestrian datasets in recent years, and looks forward to the future development trend of person re-ID. © Springer Nature Switzerland AG 2019.

关键词： Learning algorithms

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An improved person reidentification method by selective convolutional descriptor aggregation

An improved person reidentification method by selective conv...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

ISBN: (纸本)9783319987750

Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. Therefore, person re-ID is also a sub problem of image retrieval. In this paper, fine grained image retrieval method SCDA (Selective Convolutional Descriptor Aggregation) is used in person re-ID, and the framework of identification-verification is improved. The method can select useful deep descriptors, at the same time, remove background by localizing the main object. © Springer Nature Switzerland AG 2019.

关键词： Computer vision

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