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Multi-View Dynamic Kernelized Evidential Clustering

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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A Toeplitz Prior-Based Deep Learning Framework for DOA Estimation With Unknown Mutual Coupling 31

A Toeplitz Prior-Based Deep Learning Framework for DOA Estim...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Jiang, Zhuoqian Xin, Jingmin Zuo, Weiliang Zheng, Nanning Sano, Akira Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China National Engineering Laboratory for Visual Information Processing and Applications Xi'an Jiaotong University Xi'an710049 China Shunan Academy of Artificial Intelligence Zhejiang Ningbo315000 China Department of System Design Engineering Keio University Yokohama223-8522 Japan

ISBN: (纸本)9789464593600

In this paper, we explore the problem of direction-of-arrival (DOA) estimation with unknown mutual coupling using a deep learning (DL) framework which is based on Toeplitz prior. First, for source number estimation, we model it as a multilabel classification task and build a source number detection network (SNDN) to learn relevant information in the real sample covariance matrix. Next, taking full advantage of the Toeplitz structure, an ideal covariance reconstruction network (ICRN) is proposed to recover the ideal covariance matrix free from mutual coupling and noise interference. Furthermore, we design a database to store the parameters of ICRN after training on different numbers of sources, and its role is to load the corresponding parameters for ICRN according to the detection results of SNDN. Finally, the DOAs can be easily estimated from the restored covariance matrix by the MUSIC. The simulation results show our proposed approach not only outperforms the existing classical methods, but in some cases its DOA estimation accuracy can even exceed the Cramér-Rao Lower Bound. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Covariance matrix

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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

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Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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Combine Neural Radiance Fields in Different Reference Coordinates

Combine Neural Radiance Fields in Different Reference Coordi...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Weili Li, Zhongheng Guo, Yu Wang, Fei Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing China

ISBN: (纸本)9798350303759

Neural Radiance Fields (NeRF) leverage deep learning networks to restore high-quality implicit representations of 3D scenes from 2D images, surpassing traditional texture mapping in rendering quality. Typically, reconstructed images by NeRF exist in a reference coordinate system, necessitating consideration of each surface's information in a single shot. Overlapping parts in adjacent images are addressed by obtaining the camera pose through sparse COLMAP reconstruction. However, occluded regions, caused by contact with support surfaces, pose challenges for complete reconstruction. To address this, we propose a scene fusion algorithm based on NeRF, facilitating the amalgamation of neural radiance fields under different reference coordinate systems. This reduces data requirements and shooting conditions, enhancing the implicit 3D reconstruction of real objects. By estimating the translation and rotation relationship between image capture angles and the NeRF model, we fuse neural radiance fields in distinct reference coordinate systems, completing object surface information. Evaluation is conducted on both NeRF synthetic dataset and real objects. © 2023 IEEE.

关键词： Cameras

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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A Multi-View fMRI Model with Voxel Inner State for Vision Encoding

A Multi-View fMRI Model with Voxel Inner State for Vision En...

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2023 China Automation Congress, CAC 2023

作者： Li, Yueying Wu, Hao Chen, Badong Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Xi'an710049 China School of Electrical Engineering Xi'An University of Technology Xi'an710048 China

ISBN: (纸本)9798350303759

Visual encoding and decoding is an important tool to explore brain neural function, how to establish an efficient and accurate encoding model is an important issue in fMRI brain visual encoding tasks. However, due to the low signal-to-noise ratio and the high-dimensional characteristics of fMRI signal, it is difficult to extract response component from voxels which has high correlation with external image stimulus. Traditional fMRI encoding models ignore the voxel inner state and noise components in fMRI response, In this work, We present a novel approach of encoding by adding voxel mode into the encoding stage, which is ignored in previous study. We propose a multi-view voxel-wise encoding model with two input modes, independently extract voxel features and stimuli features. Our multi-view encoding model takes voxel inner state into encoding stage, and uses feature-irrelevant voxels to perform noise reduction for feature-relevant voxels simultaneously, thus reach a higher explain variance (R2) of encoding result and higher image identification accuracy of decoding result. Our work is helpful for further exploring the characteristics of visual cortex and understanding the mechanism of brain visual pathway. © 2023 IEEE.

关键词： Signal to noise ratio

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Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Camera-based 3D Object Detection

Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Cam...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zhenguo Cao, Yuanzhouhan Zhang, Hui Chen, Naiyue Ren, Chao Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University China College of Electronics and Information Engineering Sichuan University China

ISBN: (纸本)9798350368604

3D object detection is one of the fundamental perception tasks in autonomous driving. In recent years, perception in the Bird’s Eye View (BEV) has attracted tremendous attention due to its outstanding ability in preserving geometric cues. However, like other supervised learning tasks, BEV-based perception suffers from inadequate labeled training data. In this work, we propose the Vision BEV-MAE, a self-supervised learning approach for BEV perception from camera images. Specifically, we design a feature-level masking strategy to mask the mid-level representations, i.e., the encoded BEV features instead of directly masking input multi-view images, and reconstruct BEV features with a light-weight decoder. Our Vision BEV-MAE is independent of any geometric data such as point clouds, or any additional supervision, and can be easily implemented within any state-of-the-art BEV perception frameworks to improve the performance. We conduct experiments on benchmark dataset to validate our approach. © 2024 IEEE.

关键词： Self-supervised learning

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Consistency Inspection for Assembly of Bolt on Engine Using Multi-view Stereo

Consistency Inspection for Assembly of Bolt on Engine Using ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liu, Shuo Jiang, Minglv Lu, Yuanliang Wang, Jianji Zheng, Nanning National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350337020

Assembly inspection is a crucial aspect of smart manufacturing to guarantee the quality of products. To address the challenges posed by complex workshop scenes, an assembly inspection algorithm based on multi-view stereo is proposed. Specifically, to enhance the accuracy of reconstruction, the multi-view stereo method utilizes segmentation attention-assisted depth estimation and point cloud fusion to mitigate the noise in the reconstructed point cloud. And the proposed method introduces normalized depth loss to improve the reconstruction ability of foreground pixels. Futhermore, this paper presents a novel 3D point cloud-based size estimation algorithm capable of accurately estimating the size of small objects in complex working scenes. Experimental results demonstrate the efficacy of the improved multi-view stereo algorithm in assembly scenes and verify the superiority of the proposed size estimation algorithm. © 2023 IEEE.

关键词： assembly inspection multi-view stereo point cloud segmentation attention size estimation

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Byzantine-robust distributed support vector machine

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Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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