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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain 5

Motion Planning and Tracking Control Method Based on Coppeli...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Huang, Jiale Chen, Zhihua Zhong, Yongbo Zeng, Wenrui Ji, Chuanmin Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China

ISBN: (纸本)9798350307320

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATlab.and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm. © 2023 IEEE.

关键词： Motion planning

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A Control Method for Electric Cylinder Based on Three-Order ADRC

A Control Method for Electric Cylinder Based on Three-Order ...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Zhifan Jiang Zhihua Chen Lijun Tong Jiale Huang Yufei Che Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

As an executive component of robots, the precise control of electric cylinders is crucial for achieving accurate control of robots. This article proposes a position closed-loop electric cylinder control method based on three-orders active disturbance rejection control (ADRC). Firstly, model the electric cylinder system and determine its transfer function. Secondly, PID controllers and ADRC controllers were designed separately, and their stability was verified through the Routh criterion and Lyapunov method, respectively. Finally, the control effects of PID and ADRC were compared through three sets of experiments. The results show that compared to PID, ADRC is less affected by the increase in input signal frequency, has stronger anti-interference ability, and can effectively resist the influence of internal uncertainty.

关键词： Robust control Automatic frequency control PI control Uncertainty Stability criteria Transfer functions Resists PD control Robots Lyapunov methods

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Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models

arXiv

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arXiv 2024年

作者： Yang, Yuncheng Qin, Yulei Wu, Tong Xu, Zihan Li, Gang Guo, Pengcheng Shao, Hang Shi, Yuchen Li, Ke Sun, Xing Yang, Jie Gu, Yun Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Tencent YouTu Lab Shanghai200233 China

The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stable outputs. To avoid huge cost brought by manual preparation of instruction datasets and training resources up to hundreds of hours, the exploitation of open knowledge including a wealth of low rank adaptation (LoRA) models and instruction datasets serves as a good starting point. However, existing methods on model and data selection focus on the performance of general-purpose capabilities while neglecting the knowledge gap exposed in domain-specific deployment. In the present study, we propose to bridge such gap by introducing few human-annotated samples (i.e., K-shot) for advancing task expertise of LLMs with open knowledge. Specifically, we develop an efficient and scalab.e pipeline to cost-efficiently produce task experts where K-shot data intervene in selecting the most promising expert candidates and the task-relevant instructions. A mixture-of-expert (MoE) system is built to make the best use of individual-yet-complementary knowledge between multiple experts. We unveil the two keys to the success of a MoE system, 1) the abidance by K-shot, and 2) the insistence on diversity. For the former, we ensure that models that truly possess problem-solving abilities on K-shot are selected rather than those blind guessers. Besides, during data selection, instructions that share task-relevant contexts with K-shot are prioritized. For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process. Extensive experimental results confirm the superiority of our approach over existing methods on utilization of open knowledge across various tasks. Our codes will be availab.e at https://***/Yaphabates/Rocket. © 2024, CC BY.

关键词： Pipeline codes

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain

Motion Planning and Tracking Control Method Based on Coppeli...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiale Huang Zhihua Chen Yongbo Zhong Wenrui Zeng Chuanmin Ji key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China

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A Micro-Expression recognition Network Based on Attention Mechanism and Motion Magnification

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IEEE Transactions on Affective Computing 2024年

作者： Wu, Falin Xia, Yu Ma, Boyi Hu, Tianyang Yang, Jingyao Li, Haoxin Huang, Di Beihang University SNARS Laboratory School of Instrumentation and Optoelectronic Engineering Beijing China Beihang University Intelligent Recognition and Image Processing Lab. School of Computer Science and Engineering Beijing China

Micro-expressions (MEs) are spontaneous facial movements that reveal an individual's genuine emotions and play a crucial role in various domains, including lie detection, criminal analysis, mental health treatment, national security, and others. Micro-expression recognition is a highly complex aspect within the domain of affective computing, aimed at identifying subtle facial motions that are difficult for humans to discern accurately. To model the subtle facial muscle motions and the brief duration of MEs, we propose a robust micro-expression recognition (MER) network, named the attention mechanismbased motion magnification guided micro-expression recognition network (AM-MM-MER). This network consists of two primary components: the ST-MEMM network, which enhances subtle motions in micro-expression videos to reveal imperceptible facial muscle motions, and the AM-MER, which focuses on facial landmarks related to micro-expressions and incorporates novel landmark positions to extract the underlying relationships among these landmarks, thereby reducing interference from video magnification and irrelevant identity features. Extensive analysis on the CASME II and SAMM datasets demonstrates the high accuracy and effectiveness of the proposed network, achieving superior results compared to state-of-the-art methods. Ablation studies further illustrate the robustness of the proposed network. © 2010-2012 IEEE.

关键词： National security

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function lab.ls are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific lab.ls, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function lab.ls at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual lab.ling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise lab.l Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical image Segmentation

arXiv

引用

arXiv 2023年

作者： Yang, Yuncheng Wei, Meng He, Junjun Yang, Jie Ye, Jin Gu, Yun Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shanghai AI Lab Shanghai China

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it’s vital to estimate the source models’ transferability (i.e., the ability to generalize across different downstream tasks) for proper and efficient model reuse. To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method. We first analyze the drawbacks of using the existing TE algorithms for medical image segmentation and then design a source-free TE framework that considers both class consistency and feature variety for better estimation. Extensive experiments show that our method surpasses all current algorithms for transferability estimation in medical image segmentation. Code is availab.e at here. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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