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Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

作者： Haifeng Sun Qun Huang Jinbo Sun Wei Wang Jiaheng Li Fuliang Li Yungang Bao Xin Yao Gong Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Institute of Computing Technology CAS Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions.

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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Report-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide image

Report-Guided Cross-Modal Representation Learning for Predic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Qi Shi, Jun Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Hefei University of Technology School of Software Hefei230601 China Beihang University Image Processing Center School of Astronautics Beijing102206 China University of Science and Technology of China Division of Life Sciences and Medicine Department of Pathology First Affiliated Hospital of Ustc Hefei230036 China University of Science and Technology of China Intelligent Pathology Institute Division of Life Sciences and Medicine Hefei230036 China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China

ISBN: (纸本)9798350386226

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL. © 2024 IEEE.

关键词： Supervised learning

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Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

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Modelling the progression of Alzheimer's disease using Neutrosophic hidden Markov models

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Neutrosophic Sets and Systems 2023年 56卷 31-40页

作者： Nagarajan, D. Kavikumar, J. Tom, Mary Mahmud, Mufti Broumi, S. Department of Mathematics Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Mathematics and Statistics Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Campus Pagoh84600 Malaysia College of Information and Communication Technology School of Engineering & Technology |Higher Education Division CQUniversity Brisbane Campus BrisbaneQLD4000 Australia Department of Computer Science Nottingham Trent University United Kingdom United Kingdom Laboratory of Information Processing Faculty of Science Ben M’Sik University of Hassan II Casablanca Morocco

Alzheimer's disease is the primary cause of dementia. Due to the sluggish rate of progression of Alzheimer's disease, individuals have the opportunity to start receiving therapy early through routine testing. procedures since they are pricy and difficult to find. For many slowly advancing disorders, such as Alzheimer's disease (AD), the capacity to recognise changes in disease progression is essential. Machine learning methods with a high degree of modularity were used throughout the pipeline. We propose the use of Neutrosophic hidden Markov models (NHMMs) to simulate disease progression in a more thorough manner than the clinical phases of the disease. Due to the complexity and ambiguity of reality, decision-makers find it challenging to draw conclusions from precise data. Since they cannot be computed directly, the variables are encoded using a single interval Neutrosophic set. We showed that the trained HMM can imitate sickness development more accurately than the commonly acknowledged clinical phases. © (2023). All Rights Reserved.

关键词： Hidden Markov models

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An Improved IHS image Fusion Algorithm using Medoid Intensity Match and Bilateral Filter

An Improved IHS Image Fusion Algorithm using Medoid Intensit...

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2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021

作者： Tiwari, Manan Manoj Misra, Indranil Moorthi, S. Manthira Dhar, Debajyoti Bennett University Computer Science Department Uttar Pradesh Greater Noida India ISRO Signal and Image Processing Group Space Applications Centre Gujarat Ahmedabad India

ISBN: (纸本)9781665442497

image fusion is a productive way to combine multi-sensor images and extract maximum information from enhance remote sensing data. The paper describes a novel methodology to improve IHS image fusion algorithm by medoid intensity computation from multispectral images and suppress noise of panchromatic image using bilateral filter. Medoid provides an optimal representation of multispectral images and act as an intensity component in IHS color space. Bilateral filter preserves the edge information of high resolution data while reduce the noise in homogenous regions. Improved IHS image fusion algorithm by amalgamation of medoid intensity and bilateral filter found to be an effective technique to generate fused images. The technique developed is evaluated with Cartosat-1 panchromatic and Resourcesat-2 multispectral closest acquisition images. The datasets are geometrically transformed and co-registered to make it eligible for image fusion exercises. The visual assessment confirms improved IHS better retain the spatial details while preserving the spectral characteristics. Improved IHS is compared with state-of-the-art component substitution image fusion techniques and found to be perform better in almost all the standard metrics. © 2021 IEEE.

关键词： image fusion

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Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction

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IEEE Journal of Biomedical and Health Informatics 2023年第6期29卷 3842-3852页

作者： Tingting Xu Yongyong Chen Haijin Zeng Guokai Zhang Jingyong Su School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Image Processing and Interpretation Imec research group Ghent University Ghent Belgium School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling $\bf k$ - $t$ space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images. (2) they focus on preserving global information only, while ignoring the local details reconstruction such as the spatial piece-wise smoothness and sharp boundaries. To overcome these obstacles, we suggest a novel low-rank tensor decomposition approach by integrating tensor Qatar Riyal (QR) decomposition, low-rank tensor nuclear norm, and asymmetric total variation to reconstruct dMRI, named TQRTV. Specifically, while preserving the tensor inherent structure by utilizing tensor nuclear norm minimization to approximate tensor rank, QR decomposition reduces the dimensions in the low-rank constraint term, thereby improving the reconstruction performance. TQRTV further exploits the asymmetric total variation regularizer to capture local details. Numerical experiments demonstrate that the proposed reconstruction approach is superior to the existing ones.

关键词： Tensors image reconstruction Matrix decomposition Magnetic resonance imaging Imaging Transforms Discrete Fourier transforms

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Topologically Robust B-spline Reconstruction of Fibers from 3D images 8th

Topologically Robust B-spline Reconstruction of Fibers from ...

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8th Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2019

作者： Mosbach, Dennis Schladitz, Katja Hamann, Bernd Hagen, Hans Computer Graphics and HCI Group University of Kaiserslautern Kaiserslautern67663 Germany Image Processing Department Fraunhofer ITWM Kaiserslautern67663 Germany Department of Computer Science University of California DavisCA95616 United States

ISBN: (纸本)9783030834999

The micro-structure of wood-based insulation materials is analyzed to gain insight into how features on microscopic scales influence macroscopic thermal conductivity. Three-dimensional (3D) image data obtained by micro-computed tomography reveals a complex structure formed by cellulose fibers. To study the effect of geometry changes, simple B-spline representations of these fibers are highly desirable. A straightforward solution is to extract a triangulated isosurface from the 3D image and partition it into quadrilateral macro-cells with disk-like topology. For each cell, a B-spline surface is constructed by minimizing a least squares error term. However, the physical processing of the material affects the structure of the fibers. The resulting changes in surface topology cause difficulties for the quadrilateral partitioning. image processing tools can solve these topological issues, but they also impact geometry. We present a novel approach that splits geometry and topology processing of the data. It allows for topological simplification while still preserving the geometry of a scanned object. Established B-spline approximation methods are used to create a model. The involved mathematical equations are described in detail with a focus on simple implementation. Our presented results demonstrate that smooth and accurate models can be created for challenging data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fibers

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UNQA: Unified No-Reference Quality Assessment for Audio, image, Video, and Audio-Visual Content

arXiv

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arXiv 2024年

作者： Cao, Yuqin Min, Xiongkuo Gao, Yixuan Sun, Wei Lin, Weisi Zhai, Guangtao The Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

As multimedia data flourishes on the Internet, quality assessment (QA) of multimedia data becomes paramount for digital media applications. Since multimedia data includes multiple modalities including audio, image, video, and audiovisual (A/V) content, researchers have developed a range of QA methods to evaluate the quality of different modality data. While they exclusively focus on addressing the single modality QA issues, a unified QA model that can handle diverse media across multiple modalities is still missing, whereas the latter can better resemble human perception behaviour and also have a wider range of applications. In this paper, we propose the Unified No-reference Quality Assessment model (UNQA) for audio, image, video, and A/V content, which tries to train a single QA model across different media modalities. To tackle the issue of inconsistent quality scales among different QA databases, we develop a multi-modality strategy to jointly train UNQA on multiple QA databases. Based on the input modality, UNQA selectively extracts the spatial features, motion features, and audio features, and calculates a final quality score via the four corresponding modality regression modules. Compared with existing QA methods, UNQA has two advantages: 1) the multi-modality training strategy makes the QA model learn more general and robust quality-aware feature representation as evidenced by the superior performance of UNQA compared to state-of-the-art QA methods. 2) UNQA reduces the number of models required to assess multimedia data across different modalities. and is friendly to deploy to practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Audiovisual

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