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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Hao Luo Shaoyi Du Zhiqiang Tian School of Software Engineering Xi'an Jiaotong University Xi'an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics National Engineering Research Center for Visual Information and Applications Xi'an Jiaotong University Xi'an China

Existing crowd counting techniques have achieved significant progress with the emergence of deep learning. During development, emerging crowd counting methods have generally become more and more complex and enormous, enabling them to understand and process more prior knowledge from input data. However, they suffer from two major drawbacks: 1) they generally require a significant amount of labeled training samples, which is labor-intensive, and 2) they require increasing computational hardware resources, making it luxurious and impractical to apply directly in small-scale scenes. To address these issues, we formulate crowd counting as a classification problem and leverage least squares model with a novel semi-supervised strategy. Technically, we construct the least squares model based on only two regularization terms: a regression term and a discriminative relaxation term. Moreover, we propose a semi-supervised soft label correcting strategy incorporated in the model. As a result, a fast and accurate crowd counting method is achieved. Experimental results on five small-scale benchmarks demonstrate the proposed method outperforms the other competitors in terms of both regression metrics and consumed time.

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Structure Consistent Unsupervised Domain Adaptation for Driver Behavior Recognition

Structure Consistent Unsupervised Domain Adaptation for Driv...

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International Conference on Intelligent Transportation

作者： Yuying Liu Shaoyi Du Qinbo Guo Zhiyue Zhao Zhiqiang Tian Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China

Driver distraction recognition is critical for assisted driving and intelligent vehicles, while often fails in cross-domain scenarios. Previous studies only focus on behavior recognition on a single data set, ignoring the case of inconsistent distribution between training and test sets (domains). To eliminate the domain gap caused by different persons or scenarios, we propose a domain adaptation framework for cross-domain distracted driver recognition, which aims to reduce the domain gap by constraining the feature separability of the source and target domains and maximizing the similarity of the feature structures of the two domains. Firstly, we represent the structure of the domain-level distribution by the spatial relationship between classes in the feature space, where the class is represented by class prototypes to avoid the perturbation of noise samples. Secondly, we utilize the structure consistency across domains by defining the spatial relationship between classes in terms of sample-to-class distances and encourage this relationship to be consistent when adapting from source to target domain. Thirdly, we build a new cross-domain dataset for driver distraction behavior, which contains two public datasets and one self-collected dataset. Experiments demonstrate that the proposed method can effectively alleviate the driver-distracted behavior recognition problem across datasets and obtain distinguishable results compared to existing UDA methods. Code is available at: https://***/seizetheday00/Structure_consist_UDA.

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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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IEEE International Conference on Systems, Man and Cybernetics

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

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.

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CenterDA: Center-Aware Unsupervised Domain Adaptation Regularized by Class Diversity for Distracted Driver Recognition

CenterDA: Center-Aware Unsupervised Domain Adaptation Regula...

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International Conference on Intelligent Transportation

作者： Yuying Liu Shaoyi Du Shengwei Zhao Zhiqiang Tian Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China

Unsupervised domain adaptation (UDA) suffers from misleading by misclassified samples when aligning the labeled source domain and the unlabeled target domain, which causes unreliable alignment and class overlap. In this paper, we propose a center-aware UDA algorithm for inter-domain alignment, along with class diversity regularization for target intra-domain separability. First, we align the class centers of the source and target domain by weighting the sample confidence to corresponding centers, to denoise the mislabeled samples. Second, we scatter the predictions of the target domain by minimizing the entropy of intra-class predictions and decreasing the similarity of inter-class distributions, which tackles the class overlap. The proposed method not only aligns the two domains but also increases the separability of the target domain, which results in improved target data classification performance. We validate the proposed method on behavior recognition and object recognition datasets, and the experimental results show that the method greatly alleviates the domain shift compared to the latest UDA methods. Code is available at: https://***/seizetheday00/Center-Aware-DA.

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Evading Detection Actively: Toward Anti-Forensics against Forgery Localization

arXiv

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arXiv 2023年

作者： Zhuo, Long Luo, Shenghai Tan, Shunquan Chen, Han Li, Bin Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen Institute of Artificial Intelligence and Robotics for Society China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge the authenticity of an image. However, to the best of our knowledge, no attempts have been made to deceive forgery detectors at the pixel level and mis-locate forged regions. Traditional adversarial attack methods cannot be directly used against forgery localization due to the following defects: 1) they tend to just naively induce the target forensic models to flip their pixel-level pristine or forged decisions;2) their anti-forensics performance tends to be severely degraded when faced with the unseen forensic models;3) they lose validity once the target forensic models are retrained with the anti-forensics images generated by them. To tackle the three defects, we propose SEAR (Self-supErvised Anti-foRensics), a novel self-supervised and adversarial training algorithm that effectively trains deep-learning anti-forensic models against forgery localization. SEAR sets a pretext task to reconstruct perturbation for self-supervised learning. In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces. We have conducted large-scale experiments across diverse datasets. The experimental results demonstrate that, through the combination of self-supervised learning and adversarial learning, SEAR successfully deceives the state-of-the-art forgery localization methods, as well as tackle the three defects regarding traditional adversarial attack methods mentioned above. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

arXiv

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arXiv 2025年

作者： Guo, Xuejian Tian, Zhiqiang Wang, Yuehang Li, Siqi Jiang, Yu Du, Shaoyi Gao, Yue the School of Software Engineering Xi’an Jiaotong University Xi’an710049 China the College of Computer Science and Technology Jilin University Changchun130012 China the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China BNRist THUIBCS KLISS BLBCI School of Software Tsinghua University Beijing100084 China

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras’ high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by 84.28%. The code is available at https://***/lodew920/ERetinex. © 2025, CC BY.

关键词： Cameras

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Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

arXiv

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Denoising of Speech Signal using Deep Learning

Denoising of Speech Signal using Deep Learning

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Priyanka Wani Sudhanshu Uttarwar Abhijeet Dada Mote M. N. Chandan Vandana Katarwar Department of Electronics and Telecommunication Dr. D. Y. Patil Institute of Technology SPPU Pune India Sr. Software Engineer Mountain Digital Texas USA Department of Automation and Robotics JSPM’s Rajashri Shahu College of Engineering Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Speech is the primary vocalized form of natural speech. Speaking is simple, hands-free, quick, and doesn’t require any technological expertise. Speech recognition software makes it easy and comfortable for people to communicate with computers. A speech recognition system enabled this. Speech denoising using deep learning involves using neural networks to eliminate destortion from input signals, preserving the clarity of the speech. For this task, deep learning models such as CNNs or RNNs are commonly used.

关键词： Deep learning Computers Computational modeling Noise reduction Neural networks Natural languages Speech recognition software Intelligent systems

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