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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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ClickVOS: Click Video Object Segmentation

arXiv

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arXiv 2024年

作者： Guo, Pinxue Hong, Lingyi Zhou, Xinyu Gao, Shuyong Li, Wanyun Li, Jinglun Chen, Zhaoyu Li, Xiaoqiang Zhang, Wei Zhang, Wenqiang the Shanghai Engineering Research Center of AI&Robotics Academy for Engineering&Technology Fudan University Shanghai China the Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China the School of Computer Engineering and Science Shanghai University Shanghai China Engineering Research Center of AI&Robotics Ministry of Education Academy for Engineering&Technology Fudan University Shanghai China

—Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame during inference or lack the flexibility to specify arbitrary objects of interest. To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame. And we provide the extended datasets DAVIS-P and YouTubeVOS-P that with point annotations to support this task. ClickVOS is of significant practical applications and research implications due to its only 1-2 seconds interaction time for indicating an object, comparing annotating the mask of an object needs several minutes. However, ClickVOS also presents increased challenges. To address this task, we propose an end-to-end baseline approach named called Attention Before Segmentation (ABS), motivated by the attention process of humans. ABS utilizes the given point in the first frame to perceive the target object through a concise yet effective segmentation attention. Although the initial object mask is possibly inaccurate, in our ABS, as the video goes on, the initially imprecise object mask can self-heal instead of deteriorating due to error accumulation, which is attributed to our designed improvement memory that continuously records stable global object memory and updates detailed dense memory. In addition, we conduct various baseline explorations utilizing off-the-shelf algorithms from related fields, which could provide insights for the further exploration of ClickVOS. The experimental results demonstrate the superiority of the proposed ABS approach. Extended datasets and codes will be available at https://***/PinxueGuo/ClickVOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion compensation

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OpenAUC: Towards AUC-Oriented Open-Set Recognition

arXiv

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arXiv 2022年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China BDKM University of Chinese Academy of Sciences China Peng Cheng Laboratory China

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden’s index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness. Project Page: https://***/wang22ti/OpenAUC. © 2022, CC BY.

关键词： Forecasting

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Real-Time Data Collection of Wsn Based on Trajectory Optimization of Laser-Charged Uav

SSRN

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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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BCIM: Budget and capacity constrained influence maximization in multilayer networks

arXiv

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arXiv 2024年

作者： Zhang, Su-Su Liu, Chuang Wang, Huijuan Chen, Yang Zhan, Xiu-Xiu Research Center for Complexity Sciences Hangzhou Normal University Hangzhou311121 China College of Media and International Culture Zhejiang University Hangzhou310058 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Influence maximization (IM), which aims to select a set of seed nodes that can maximize their influence within a network, can be applied to various domains, such as viral marketing, disease control, and political campaigns. One of the extended IM problems, i.e., budgeted Influence Maximization (BIM), adds a budgeted constraint, which considers that different nodes may have different costs. However, the definition of the current BIM problem has limitations, as it only imposes a budget constraint. This may lead to solutions with a large number of low-cost nodes that are not applicable to real-world scenarios. Furthermore, since users can transmit information on multiple social networking platforms, solving the BIM problem across different platforms could better optimize resource usage. To address these issues, we propose a Budget and Capacity Constrained Influence Maximization (BCIM) problem in multilayer networks and design a Multilayer Multi-Population Genetic Algorithm (MMGA) to solve it. In MMGA, a repair module addresses budget constraints, while a multi-population parallel module handles capacity (seed size) constraints. Additionally, the initialization module, which considers multilayer network properties, and the multi-population parallel module significantly enhance the algorithm's efficiency. Extensive experiments upon various synthetic and empirical multilayer networks indicate that MMGA achieves at least a 10% improvement in spreading performance under budget and capacity constraints compared to baseline methods extended from various classical IM problems. The proposed BCIM problem opens up a new direction in influence maximization, and the proposed method offers an effective and efficient framework to the problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Costs

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A Novel Sensitization Mechanism of Stress-Electric-Coupled Biosensor Originated in Magnetostrictive Effect

SSRN

引用

SSRN 2022年

作者： Sang, Shengbo Ge, Yang Ji, Jianglong Yuan, Zhongyun Dong, Xiushan Zhou, Chuangang Xiao, Pengli Tan, Qiuyun Zhao, Dong MicroNano System Research Center Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education College of Information and Computer Taiyuan University of Technology Taiyuan030024 China School of Electrical Engineering College of Engineering Science Hanyang University Seoul04763 Korea Republic of Shanxi Bethune Hospital Taiyuan030024 China Six-dimensional Artificial Intelligence Biomedical Research Institute Taiyuan030024 China

The stress-electric coupling based on receptor functionalization provides a new paradigm for the development of biosensor. However, the gravity factor of sensitive element usually shows difficult to be avoided due to its suppression to biosensor sensitivity. Herein, a magnetic-sensitization biosensing system with multi-physical field coupling of stress, magnetism and electricity was proposed to highly detect biomolecules. The gravitational interference of sensitive membrane was conquered by doping the TbDyFe with magnetostrictive effect into polydimethylsiloxane (PDMS) and applying magnetic field, showing great contributions to magnetic sensitization. The magnetic sensitization of the biosensor was proved by the collaboration of experiment and simulation. The detections of urine protein and urine sugar associated with kidney disease were realized with limits of detection (LOD) of 0.21 ng/mL and 0.45 nM by using the magnetic-sensitization biosensor. The prepared biosensor exhibited the high specificity, selectivity, stability and repeatability. Especially, the HSA and glucose in real samples can be accurately detect, showing good practicability of the prepared biosensor. This study provides a way to improve the sensitivity of the mechanical biosensor. © 2022, The Authors. All rights reserved.

关键词： Silicones

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Direct speech-to-image translation

arXiv

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arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Multi-granulation variable precision rough set based on limited tolerance relation

引用

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 63-74页

作者： Wan, Renxia Yao, Yonghong Kumar, Hussain School of Mathematics and Information Science North Minzu University Ningxia750021 China HongYang Institute for Big Data in Health Fuzhou Fujian350028 China School of Mathematical Sciences Tiangong University Tianjin300387 China The Key Laboratory of Intelligent Information and Data Processing of NingXia Province North Minzu University and Health Big Data Research Institute of North Minzu University Yinchuan750021 China Machine vision and pattern recognition lab University of Regina ReginaSKS4S0A2 Canada

In this paper, the combination of the variable precision rough set and the limited tolerance relation under multi-granularity is explored. As an extension of rough set model, Multi-granularity variable precision limited tolerance rough set model is con-structed. Properties of upper and lower approximation operator and detailed structure of object class through threshold are discussed. Theoretical proofs are given to show the rationality and superiority of the proposed model. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Granulation

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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

arXiv

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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