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Small Object Oriented Pose Estimation with Structural Similarity Constraint

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Han, Le Zhao, Lei Zhang, Han Song, Zhiying Wang, Pengfei Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies (QAAS) Zhejiang Hangzhou 310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou 310007 China Zhejiang University School of Software Technology College of Computer Science and Technology China College of Computer Science and Technology Zhejiang University Hangzhou Ningbo China Zhejiang University Qiushi Academy for Advanced Studies Hangzhou China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou 310007 China Collaborative Innovation Center for Artificial Intelligence by MOE Zhejiang Provincial Government ZJU Hangzhou 310007 China Bengbu University School of Computer and Information Engineering Bengbu 233030 China

Remote measurement of animal pose and motion is essential in neuroscience and robotics. However, subjects like rats often appear as small objects in open environments, occupying a minimal field of view. The scarcity of visual information and high susceptibility to errors presents significant challenges for remote measurement of the pose of these objects. This study introduces SalPose, a pose estimation model designed for small objects, addressing these challenges through rapid perception of small objects and keypoints structural information mining. First, we enhance the perception of weak visual features by integrating saliency detection and constructing pseudo-saliency maps with annotated keypoints. Second, a scale-invariant structural similarity constraint is developed to facilitate the learning of intrinsic keypoint features and reduce the impact of small object size. Additionally, to address the crucial gap in small-sized animal behavior analysis datasets in open environments, we introduce RP-5.7K, a rat pose dataset comprising exclusively small object data from the aerial perspective. Extensive experiments on the proposed dataset and two public datasets demonstrate the superiority of the proposed model. This study presents a novel method for remote motion measurement using visual information, mitigating the high sensitivity of small objects to distance-based measurement errors. The contributed dataset is valuable for visual measurement tasks involving small objects and animal behavior analysis. The RP-5.7K dataset is available here. © 1963-2012 IEEE.

关键词： Animal Motion Measurement Pose Estimation Remote Measurement Small Object

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Prototypes as Anchors: Tackling Unseen Noise for online continual learning

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Neural Networks 2025年 190卷

作者： Shao-Yuan Li Yu-Xiang Zheng Sheng-Jun Huang Songcan Chen Kangkan Wang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China State Key Lab. for Novel Software Technology Nanjing University Nanjing 211106 PR China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Nanjing University of Aeronautics and Astronautics Nanjing 211106 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China

In the context of online class-incremental continual learning (CIL), adapting to label noise becomes paramount for model success in evolving domains. While some continual learning (CL) methods have begun to address noisy data streams, most assume that the noise strictly belongs to closed-set noise—i.e., they follow the assumption that noise in the current task originates classes within the same task. This assumption is clearly unrealistic in real-world scenarios. In this paper, we first formulate and analyze the concepts of closed-set and open-set noise, showing that both types can introduce unseen classes for the current training classifier. Then, to effectively handle noisy labels and unknown classes, we present an innovative replay-based method Prototypes as Anchors (PAA), which learns representative and discriminative prototypes for each class, and conducts a similarity-based denoising schema in the representation space to distinguish and eliminate the negative impact of unseen classes. By implementing a dual-classifier architecture, PAA conducts consistency checks between the classifiers to ensure robustness. Extensive experimental results on diverse datasets demonstrate a significant improvement in model performance and robustness compared to existing approaches, offering a promising avenue for continual learning in dynamic, real-world environments.

关键词： Continual learning Noisy label Open-set noise

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SpEx+: A complete time domain speaker extraction network 21

SpEx+: A complete time domain speaker extraction network

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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Neural Blind Deconvolution Using Deep Priors

Neural Blind Deconvolution Using Deep Priors

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Dongwei Ren Kai Zhang Qilong Wang Qinghua Hu Wangmeng Zuo Tianjin Key Lab of Machine Learning College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on fixed and handcrafted priors that certainly are insufficient in characterizing clean images and blur kernels, and usually adopt specially designed alternating minimization to avoid trivial solution. In contrast, existing deep motion deblurring networks learn from massive training images the mapping to clean image or blur kernel, but are limited in handling various complex and large size blur kernels. To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution. In particular, we adopt an asymmetric Autoencoder with skip connections for generating latent clean image, and a fully-connected network (FCN) for generating blur kernel. Moreover, the SoftMax nonlinearity is applied to the output layer of FCN to meet the non-negative and equality constraints. The process of neural optimization can be explained as a kind of ''zero-shot" self-supervised learning of the generative networks, and thus our proposed method is dubbed SelfDeblur. Experimental results show that our SelfDeblur can achieve notable quantitative gains as well as more visually plausible deblurring results in comparison to state-of-the-art blind deconvolution methods on benchmark datasets and real-world blurry images. The source code is publicly available at https://***/csdwren/SelfDeblur.

关键词： Kernel Deconvolution Optimization Electronics packaging machine learning Estimation Convolution

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Multi-Stage Speaker Extraction with Utterance and Frame-Level Reference Signals

Multi-Stage Speaker Extraction with Utterance and Frame-Leve...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Meng Ge Chenglin Xu Longbiao Wang Eng Siong Chng Jianwu Dang Haizhou Li Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore National University of Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781728176055;9781728176062

Speaker extraction requires a sample speech from the target speaker as the reference. However, enrolling a speaker with a long speech is not practical. We propose a speaker extraction technique, that performs in multiple stages to take full advantage of short reference speech sample. The extracted speech in early stages is used as the reference speech for late stages. For the first time, we use frame-level sequential speech embedding as the reference for target speaker. This is a departure from the traditional utterance-based speaker embedding reference. In addition, a signal fusion scheme is proposed to combine the decoded signals in multiple scales with automatically learned weights. Experiments on WSJ0-2mix and its noisy versions (WHAM! and WHAMR!) show that SpEx++ consistently outperforms other state-of-the-art baselines.

关键词： Conferences Signal processing Acoustics Noise measurement Time-domain analysis Speech processing

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Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation

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Nature communications 2024年第1期15卷 9742页

作者： Feiyan Tian Ying Zhang Kenneth E Schriver Jia Ming Hu Anna Wang Roe Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. hujiaming@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. hujiaming@***. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. annawang@***. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. annawang@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. annawang@***. National Key Laboratory of Brain and Computer Intelligence Zhejiang University Hangzhou 310058 China. annawang@***.

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A Data Replication Placement Strategy for the Distributed Storage System in Cloud-Edge-Terminal Orchestrated Computing Environments

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IEEE Internet of Things Journal 2025年

作者： Chen, Peng Zheng, Mengke Du, Xin Bilal, Muhammad Lu, Zhihui Duan, Qiang Xu, Xiaolong Nanjing University of Information Science and Technology School of Software and Technology Nanjing210044 China Fudan University School of Computer Science Shanghai200433 China Zhejiang University School of Software Technology Zhejiang 310027 China Zhejiang University State Key Lab of Brain-Machine Intelligence Zhejiang 310027 China Lancaster University School of Computing and Communications LancasterLA1 4WA United Kingdom Shanghai Blockchain Engineering Research Center Shanghai200433 China Pennsylvania State University School of Information Sciences and Technology State CollegePA16802 United States

Cloud-edge-terminal orchestrated computing, as an expansion of cloud computing, has sunk resources to the edge nodes and terminal equipment, which can provide high-quality services for delay-sensitive applications and reduce the cost of network communication. Due to the high volume of data generated by Internet of Things (IoT) devices and the limited storage capacities of edge nodes, a significant number of terminal devices are now being considered for utilization as storage nodes. However, because of the heterogeneous storage capacity and reliability of these hardware devices and the different data requirements of user services, the performance and storage reliability of applications deployed in cloud-edge-terminal orchestrated computing environments have become urgent problems to be solved. Especially, for a distributed storage system in these environments, it is required to ensure reliable storage of the generated data and its’ replications. In this paper, we first implement a distributed storage system and construct a data replication placement model. Then, based on the constructed model, we formulate the data replication placement problem and design a data replication placement strategy called DRPS to solve it. The DRPS covers a ranks-based replication storage node selection algorithm and a greedy load balancing algorithm, which can select appropriate hardware devices for different data requirements of services and is implemented in the data storage system to store replications and balance loads. We design extensive experiments to verify the effectiveness of DRPS. The results indicate that the proposed strategy outperforms other state-of-the-art algorithms in terms of system delay reduction by 39.9%, an increase of 43.3% in the replication numbers, a 27.5% improvement in memory utilization, and a reduction of unreliability rate by 82.0%. © 2014 IEEE.

关键词： Cloud storage

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Multi-label Classification with High-rank and High-order label Correlations

arXiv

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Plant Leaf Recognition Network Based on Feature Learning and Metric Learning 16th

Plant Leaf Recognition Network Based on Feature Learning and...

引用

16th International Conference on Intelligent Computing, ICIC 2020

作者： Yang, Hongwei Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhao, Zhongqiu Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuro-Science and Brain-Inspired Intelligence Ministry of Education Shanghai China College of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783030607982

Plant image recognition is an important thing for protecting plants, protecting the environment, and protecting nature. Recently, most models in the field of plant leaf recognition make classification after extracting global features. In this paper, we propose a plant leaf recognition model based on metric learning. Metric learning calculates the similarity of the extracted feature vectors to obtain the distance between different sample features, so as to determine whether similar pictures belong to the same category, and then achieve the classification effect. In this study, feature triplet are used for metric learning, and the loss function we used is triplet-loss. © 2020, Springer Nature Switzerland AG.

关键词： Image recognition

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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