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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Yinxuan Huang Chengmin Gao Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

ISBN: (纸本)9798331314385

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.

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The Problems and Analysis of Artificial Intelligence Specialty Construction in Universities Under the Present Situation of Artificial Intelligence Development 8th

The Problems and Analysis of Artificial Intelligence Special...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Fu, Weina Liu, Shuai Institute of Information Science and Engineering Hunan Normal University Hunan Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha410081 China

ISBN: (纸本)9783031211638

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Personnel training

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Environmental Features Assessment Network Aided Deep Reinforcement Learning for Quadrupedal Locomotion in Tough Terrain

Environmental Features Assessment Network Aided Deep Reinfor...

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2023 China Automation Congress, CAC 2023

作者： Zuo, Guoyu Wang, Yong Huang, Gao Yu, Shuangyue Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350303759

Quadrupedal robots have more powerful locomotion capabilities compared to other types of mobile robots. However, the complexity of their controllers and the need to adapt to a wide variety of terrains poses a huge difficulty in the design of the controllers. Recently, deep reinforcement learning (DRL) has been widely used in quadruped robot controller design but still relies on complex and reliable sensing frameworks. In this paper, we propose a motion controller training framework. The framework includes an Environmental Feature Assessment Network (EFANet) to guide the action output of the policy network, and an asymmetric actor-critic structure to help the policy network infer terrain features. We also introduce a strategic trajectory generator into our framework to prevent the robot from generating abnormal gaits. The proposed framework for learning quadrupedal locomotion allows quadrupedal robots to traverse challenging terrains with limited sensors. Finally, the proposed approach is tested and evaluated in a simulation environment using the A1 quadruped robot model. © 2023 IEEE.

关键词： Reinforcement learning

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Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human keypoint Position and Human Body Detection

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Journal of Shanghai Jiaotong University (Science) 2023年 1-11页

作者： Tahir, Rizwan Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Recent multimedia and computer vision research has focused on analyzing human behavior and activity using images. Skeleton estimation, known as pose estimation, has received a significant attention. For human pose estimation, deep learning approaches primarily emphasize on the keypoint features. Conversely, in the case of occluded or incomplete poses, the keypoint feature is insufficiently substantial, especially when there are multiple humans in a single frame. Other features, such as the body border and visibility conditions, can contribute to pose estimation in addition to the keypoint feature. Our model framework integrates multiple features, namely the human body mask features, which can serve as a constraint to keypoint location estimation, the body keypoint features, and the keypoint visibility via mask region-based convolutional neural network (Mask-RCNN). A sequential multi-feature learning setup is formed to share multi-features across the structure, whereas, in the Mask-RCNN, the only feature that could be shared through the system is the region of interest feature. By two-way up-scaling with the shared weight process to produce the mask, we have addressed the problems of improper segmentation, small intrusion, and object loss when Mask-RCNN is used, for instance, segmentation. Accuracy is indicated by the percentage of correct keypoint, and our model can identify 86.1% of the correct keypoints. © 2023, Shanghai Jiao Tong University.

关键词： Convolutional neural networks

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Underwater Image Enhancement via Multi-color Space Correction and Fusion 3

Underwater Image Enhancement via Multi-color Space Correctio...

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3rd International Conference on Electronic information Engineering and Computer, EIECT 2023

作者： Zhou, Chenyu Chen, Bo Zhang, Ying Chen, Wensheng Pan, Binbin Ji, Jing Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350357707

Underwater image enhancement, as an important branch of image processing, has attracted the attention of many scholars in recent years. Due to selective scattering and degradation of light in water, images captured underwater are generally of low quality. To improve the accuracy of underwater image details and color, we propose a novel underwater image enhancement model, which is based on a Multi-color space fusion (MSF) strategy and assigns corresponding weights to each color channel for fusion. Qualitative and quantitative comparisons show that the proposed method can effectively remove image blur, achieve color correction, and significantly improve image sharpness. © 2023 IEEE.

关键词： Color

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Food computing for Nutrition and Health

Food Computing for Nutrition and Health

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International Conference on Data Engineering Workshops

作者： Shuqiang Jiang Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

Dietary intake is the main source of human nutrition and thus an important material foundation for maintaining human health. Food computing applies computational approaches to intelligently obtain dietary categories, ingredients, and nutrient intake from dietary data, and then provides dietary recommendations to users based on various dietary factors. Therefore, it can achieve intelligent dietary management to assist in solving human health issues. For its significant impact on human health, it has received more attention from both academia and industry, and has made rapid development, especially with the booming Artificial Intelligence (AI). This talk will first introduce the definition, methods, tasks and applications of food computing. Then we will discuss its various tasks, ranging from vision-based multi-granularity food analysis, multimodal recipe analysis, food knowledge graph construction and dietary recommendation. Finally, we will point out future research directions on food computing, such as large food language/multimodal models and the emerging interdisciplinary field AI4Food from the synergy between AI and food science. We expect that this talk will provide the researchers with a more comprehensive understanding of the field, and provide perspectives for the next wave of development in this rapidly growing field.

关键词： Industries Conferences Computational modeling Knowledge graphs Data engineering Distance measurement Artificial intelligence

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Broadband external rejection miniaturized SIW filter based on C-shaped symmetric structure

Broadband external rejection miniaturized SIW filter based o...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Xiao, Shuang Li, Minquan Qu, Xin Li, Chen Zhang, Zufeng Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Liaoyang Province Hefei230601 China

ISBN: (纸本)9781665452366

To effectively improve the frequency selectivity of the filter, a substrate integrated waveguide (SIW) filter with miniaturization and out-of-band rejection was designed. To achieve the desired filter response, symmetrical C-shaped resonators are etched on the top and bottom layers of the SIW filter. The C-shaped resonator etched on the top layer of SIW achieves miniaturization and filtering characteristics. In order to improve the frequency response, a two-stage SIW filter and a SIW filter with coupled gap-ring defected ground structure (CGRDGS) are proposed, and an equivalent circuit is given to verify the transmission response. The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication. © 2022 IEEE.

关键词： Resonators

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Ranking the Spreading Influence of Nodes in Complex Networks by Combining Local Average Weight and Average Normalized Link Entropy 4

Ranking the Spreading Influence of Nodes in Complex Networks...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Fan, Kaiyu Yang, Fan Tu, Dexin Tang, Xinlai Mao, Xiuyong Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology Key Laboratory of Intelligent Information Processing and Graph Processing Liuzhou545006 China

ISBN: (纸本)9798350343618

Ranking the spreading influence of nodes is of great importance for controlling the spreading process on networks. Although a few measures are proposed for ranking nodes in weighted networks, it is still a challenge in accurately and efficiently ranking the spreading influence of nodes in weighted networks. In this paper, by utilizing both of the local and global topological structure information, a novel method is proposed. First, we define a local average weight (LAW) to describe the local topological structure information of nodes. Second, partly based on the link diversity, we define an average normalized link entropy (ANLE) to describe the global topological structure information of nodes. Further, by combining the LAW and ANLE, we propose a novel ranking measure termed as LAW-ANLE (LA) centrality. The proposed LA is verified by extensive experiments on a series of networks, and the results show that the LA is feasible and effective in accurately ranking the spreading influence of nodes. © 2023 IEEE.

关键词： Complex networks

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Repetitive Path Planning with Experience-Based Bidirectional RRT 1

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International Conference on Robots for Space Applications in Orbital Stations, TORVEASTRO 2023

作者： Zuo, Guoyu Wu, Chun Huang, Gao Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (数字)9783031284472

ISBN: (纸本)9783031284465

Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is mainly static, but it may contain some movable obstacles that the robot needs to avoid, and the planning problems that the robot will encounter are very similar. However, most current sampling-based motion planning methods ignore the correlation among tasks, and instead plan each task instance from scratch, or assume that the environment is highly structured. In order to overcome this problem, this paper proposes an experience-based bidirectional rapidly-exploring random trees* (EBRRT*) for repetitive path planning in semi-structured environments. First, an adaptive sampler learned from the demonstration paths by a Gaussian mixture model (GMM) is constructed, which can guide the planner to generate samples in task-related regions. Second, an experience graph is proposed to capture the exploration of configuration space by previous similar tasks. Subsequent tasks can retrieve locally valuable information from the graph to avoid exploring the configuration space repeatedly. In addition, the validity of edges contained in the experience graph is lazily verified during the planning process so that the experience graph can adapt to small environmental changes. Finally, the proposed method is verified in a 2D simulation environment and a 7-DOF manipulator scene. Experimental results show that the proposed method can return a high-quality path in less time than other state-of-the-art sample-based motion planning for repetitive path planning in semi-structured environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion planning

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An RFI Mitigation Pipeline for CRAFTS Multi-beam Data Based on Signal Cross-Correlation Function and SumThreshold Algorithm

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Research in Astronomy and Astrophysics 2023年第5期23卷 160-171页

作者： Zong-Hao Chen Shan-Ping You Xu-Hong Yu Pei Wang Di Li Xiao-Yao Xie Zhi-Jie Liu Chun-Qing Wang Peng Zeng Bin Zhang Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal UniversityGuiyang 550001China CAS Key Laboratory of FAST NAOCChinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa

The increasing radio frequency interference(RFI)is a well-recognized problem in radio astronomy *** and Fast Radio Bursts(FRBs)are high-priority science targets of the ongoing Commercial Radio Astronomy FAST Survey(CRAFTS).To improve the quality of RFI removal in searches of pulsars and FRBs based on CRAFTS multi-beam data,we here propose an intuitive but powerful RFI mitigation pipeline(CCF-ST).The“CCF-ST”is a spatial filter constructed by signal cross-correlation function(CCF)and Sum-Threshold(ST)*** RFI marking result is saved in a“mask”file,a binary format for RFI masks in *** known pulsars,PSR B0525-21,PSR B0621-04,and PSR J0943+2252 from CRAFTS L-band 19 beams data are used for evaluation of the performance of CCF-ST in comparison with other methods,such as PRESTO’s“rfifind”,ArPLS-ST and *** result shows that CCF-ST can reduce effective data loss rate and improves the detected signal-to-noise ratio of the pulsations by~26%and~18%respectively compared with PRESTO’s“rfifind”and *** CCF-ST also has the advantage of low computational cost,e.g.,reducing the time consumption by~40%and memory consumption by~90%compared with *** expect that the new RFI mitigation and analysis toolkit(CCF-ST)demonstrated in this paper can be applied to CRAFTS and other multi-beam telescope observations to improve the data quality and efficiency of pulsar and FRB searches.

关键词： methods data analysis-(stars:)pulsars general-stars neutron

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