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SSRN 2022年

作者： Lv, Kexin He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China

Generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where data samples are distributively stored across agents. The distributed GEP is reformulated as a consensus optimization, but the presence of its quadratic inseparable constraint makes the considered problem more challenging. To deal with it, a sequential method is proposed combined with the alternating direction method of multipliers, which requires communication between couples of nodes. Theoretical analysis shows the proposed algorithm will converge to the set of stationary solutions. And the numerical experiments on synthetic and real-world datasets validate that the approximated solution is competitive to the ground truth. © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN

arXiv

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arXiv 2024年

作者： Zhou, Zhen Li, Jiachen Xin, Hongyi Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseudotime and gene interaction networks, especially in multi-branch differentiation scenarios. We present single-cell Piecewise Network (scPN), a novel high-dimensional dynamical modeling approach that iteratively extracts temporal patterns and inter-gene relationships from scRNA-seq data. To tackle multi-branch differentiation challenges, scPN models gene regulatory dynamics using piecewise gene-gene interaction networks, offering an interpretable framework for deciphering complex gene regulation patterns over time. Results on synthetic data and multiple scRNA-seq datasets demonstrate the superior performance of scPN in reconstructing cellular dynamics and identifying key transcription factors involved in development compared to existing methods. To the best of our knowledge, scPN is the first attempt at modeling that can recover pseudotime, velocity fields, and gene interactions all at once on multi-branch datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Transcription

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Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Noisy Image Segmentation Algorithm Amalgamating Weighted Local Information

SSRN

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SSRN 2024年

作者： Huang, Chengquan Lei, Huan Chen, Yang Cai, Jianghai Qin, Xiaosu Peng, Jialei Zhou, Lihua Zheng, Lan Engineering Training Center Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang550025 China

The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image segmentation. Therefore, we propose a new noisy image segmentation algorithm based on weighted local information for interval type-2 enhanced possibilistic fuzzy C-means clustering. Firstly, a new possibilistic fuzzy weighted local information factor, which amalgamates the mutually guided image filtering, is devised to control the relationship between mutually guided image filtering and the original image utilizing the absolute difference image between the original image and mutually guided image filtering. Additionally, two weighted sum functions are also introduced into the new possibilistic fuzzy weighted local information factor to calculate the greyscale differences between the current pixel and its neighborhood pixels. Secondly, the distance of the objective function is corrected using mutually guided image filtering to obtain the local features and global information of the image. Finally, this paper introduces the new possibilistic fuzzy weighted local information factor into the interval type-2 possibilistic fuzzy C-means clustering algorithm and proposes a new interval type-2 enhanced possibilistic fuzzy C-means clustering algorithm with local information and mutually guided image filtering constraints to improve noisy image segmentation results. Through extensive experiments on noisy synthetic images and noisy real images, the proposed algorithm can achieve higher performance and more accurate segmentation results compared with several existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

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IFAC-PapersOnLine 2023年第2期56卷 3366-3372页

作者： Yongkang Xu Zhihua Chen Shoukun Wang Junzheng Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing CO 100081 P.R.China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology School of Information Engineering Nanchang Hangkong University Nanchang CO 330063 P.R. China

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

关键词： Quadrotor UAV Mobile Autonomous recovery Target localization Falling point tracking Buffering landing

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Hybrid Data-Free Knowledge Distillation 39

Hybrid Data-Free Knowledge Distillation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

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

ISBN: (纸本)157735897X

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. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Personnel training

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Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

arXiv

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven LeuvenB-3001 Belgium

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world data sets. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Gait recognition Algorithm GCGait based on Skeleton

Gait Recognition Algorithm GCGait based on Skeleton

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ru Ma Jikai Zhang Mengyu Jia Xiaoqi Lv School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China School of Information Engineering Inner Mongolia University of Technology Hohhot China

To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method.

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Face Forgery Detection Algorithm Based on Improved MobileViT Network

Face Forgery Detection Algorithm Based on Improved MobileViT...

引用

6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Tiantian Wang Xiaoqi Lu School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Institute of Information Engineering Inner Mongolia University of Technology Hohhot China

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization.

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Infrared Traffic Object Detection Network for Edge Device Deployment

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Guangxue Xuebao/Acta Optica Sinica 2025年第9期45卷

作者： Han, Yulan Chen, Deao Wu, Tong Liu, Xianlu Lan, Chaofeng Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Heilongjiang Harbin150080 China

Objective The rapid advancement of intelligent transportation systems has increased the demand for precise and swift traffic object detection capabilities, particularly under challenging low-light conditions and complex backgrounds. Infrared (IR) imaging technology has emerged as a critical tool for such scenarios due to its ability to capture heat signatures, thus enabling reliable detection even in darkness or through obscurants. However, traditional IR detection methods are often hindered by high computational complexity, large parameter sizes, and dependency on high-performance computing resources, which makes them unsuitable for deployment on resource-limited mobile devices. We introduce the Edge-DETR network, specifically designed for the efficient and accurate detection of traffic objects in IR imagery on edge devices. Methods The Edge-DETR network is an innovative detection framework that builds upon the RT-DETR model with several enhancements. It incorporates a context anchor attention multistage efficient layer aggregation network (CAA-MELAN) to adaptively extract multi-scale features and understand global dependencies, thereby addressing the challenges posed by varying object sizes and environmental dynamics. Additionally, a global information supplement module (GISM) is employed for effective feature downsampling, which ensures the preservation of essential spatial information while reducing computational load. The cross-level feature fusion module (CFFM) facilitates interaction among features at different scales, enhancing the network’s capability to integrate high-level semantic information with low-level spatial details. The HiLo attention mechanism is introduced to optimize intra-scale feature interaction, with a focus on target contours while minimizing parameter and computational requirements. To address the detection of objects with complex shapes and sizes, a Shape-IoU loss function is utilized, which accounts for the shape and size of bounding b

关键词： Thermography (imaging)

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Natural image matting based on image inpainting

Natural image matting based on image inpainting

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Computer Graphics, Image and Virtualization (ICCGIV), International Conference on

作者： Yuan Zhang Mian Tan Zhulian Zhou Yuan Yang Yihui Liang Fujian Feng Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science University of Electronic Science and Technology China Zhongshan China

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions.

关键词： Degradation Deep learning Computer vision Image processing Refining Neural networks Production

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