检索结果-内蒙古大学图书馆

32nd ACM International Conference on Multimedia, MM 2024

作者： Gao, Yingjie Zhang, Yanan Huang, Ziyue Liu, Nanqing Huang, Di SKLCCSE Beihang University Beijing China SIST Southwest Jiaotong University Chengdu China Zhejiang Industrial Big Data and Robot Intelligent System Key Lab. Hangzhou China

ISBN: (纸本)9798400706868

In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated data. The fine-tuning based paradigm is currently dominating this field, where detectors are initially pre-trained on base classes with sufficient samples and then fine-tuned on novel ones with few samples, but the scarcity of lab.led samples of novel classes greatly interferes precisely fitting their data distribution, thus hampering the performance. To address this issue, we propose a new framework for FSOD, namely Prototype-based Soft-lab.ls and Test-Time Learning (PS-TTL). Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances from test data, allowing detectors to learn better representations and classifiers for novel classes. Furthermore, we notice that even though relatively low-confidence pseudo-lab.ls exhibit classification confusion, they still tend to recall foreground. We thus develop a Prototype-based Soft-lab.ls (PS) strategy through assessing similarities between low-confidence pseudo-lab.ls and category prototypes as soft-lab.ls to unleash their potential, which substantially mitigates the constraints posed by few-shot samples. Extensive experiments on both the VOC and COCO benchmarks show that PS-TTL achieves the state-of-the-art, highlighting its effectiveness. The code and model are availab.e at https://***/gaoyingjay/PS-TTL. © 2024 ACM.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

VSLink: A Fast and Pervasive Approach to Physical Cyber Space Interaction via Visual SLAM

VSLink: A Fast and Pervasive Approach to Physical Cyber Spac...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Han Zhou Jiaming Huang Hongchang Fan Geng Ren Yi Gao Wei Dong Zhejiang Key Lab of Service Robot College of Computer Science Zhejiang University Alibaba Group

With the fast growth of the Internet of Things, people now are surrounded by plenty of devices. To achieve efficient interaction with these devices, human-device interaction technologies are evolving. Because existing methods (mobile App) require users to remember the mapping between the real-world device and the digital one, an important point is to break such a gap. In this paper, we propose VSLink, which offers human-device interaction in an Augmented-Reality-like manner. VSLink achieves fast object identification and pervasive interaction for fusing the physical and cyberspace. To improve processing speed and accuracy, VSLink adopts a two-step object identification method to locate the interaction targets. In VSLink, visual SLAM and object detection neural networks detect stable/-movable objects separately, and detection prior from SLAM is sent to neural networks which enables sparse-convolution-based inference acceleration. VSLink offers a platform where the user could customize the interaction target, function, and interface. We evaluated VSLink in an environment containing multiple objects to interact with. The results showed that it achieves a 33% network inference acceleration on state-of-the-art networks, and enables object identification with 30FPS video input.

关键词： Visualization Simultaneous localization and mapping Neural networks Cyberspace Object detection Sensors Mobile applications

来源：评论

学校读者我要写书评

暂无评论

Movable Antenna-Assisted Integrated Sensing and Communication Systems

引用

IEEE Transactions on Wireless Communications 2025年

作者： Jiang, Chengjun Zhang, Chensi Huang, Chongwen Ge, Jianhua Niyato, Dusit Yuen, Chau Xidian University State Key Lab. of Integrated Service Networks Xi’an China Zhejiang University College of Information Science and Electronic Engineering China Xidian University State Key Laboratory of Integrated Service Networks China Zhejiang Provincial Key Laboratory of Info. Proc China Nanyang Technological University College of Computing and Data Science Singapore Nanyang Technological University School of Electrical and Electronics Engineering Singapore

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources. © 2002-2012 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

EdgeMan: Ensuring Real-Time service for Containerized Edge Systems

EdgeMan: Ensuring Real-Time Service for Containerized Edge S...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Wenzhao Zhang Wei Dong Geng Ren Yi Gao Zhejiang Key Lab of Service Robot College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies Alibaba Group

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, ensuring realtime service at the containerized edge systems (CESs) remains a challenge. In this paper, we propose Edgeman,a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of Edgemanis reduced 85.9% on average compared with existing methods in both industry and academia.

关键词： Industries Sensitivity Job shop scheduling Scheduling algorithms Social networking (online) Emulation Containers

来源：评论

学校读者我要写书评

暂无评论

Secure Communication Based on Reconfigurable Intelligent Surface in Satellite Communications with Similar Channels

Secure Communication Based on Reconfigurable Intelligent Sur...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Chengjun Jiang Chensi Zhang Chongwen Huang Jianhua Ge Chau Yuen The State Key Lab. of Integrated Service Networks Xidian University Xi'an China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we investigate physical layer security (PLS) for terrestrial RIS-assisted multibeam satellite commu-nications with similar channels. Deploying the reconfigurable intelligent surface (RIS) in each beam, and we use inter-beam in-terference and the channel variability from the RIS to the satellite user and eavesdroppers to enhance satellite security. Specifically, considering the constraints of the secrecy rate, we formulate a problem to minimize the total power by jointly optimizing the satellite beamforming and the RIS beamforming. To solve the problem, the original problem is decoupled into two non-convex optimization sub-problems, i.e., the active and the passive beamforming optimization problem. Then, semidefinite relax-ation (SDR) is used to solve the active beamforming optimization problem. The maximum ratio transmission-based beamforming and SDR are employed separately to solve passive beamforming optimization problem in each beam. The alternating optimization (AO) is utilized to solve the original problem and the convergence is proven. We have carried out simulations to evaluate the effectiveness of our proposed approach, the results show that the proposed algorithms have superior system performance.

关键词： Vehicular and wireless technologies Satellites Array signal processing System performance Reconfigurable intelligent surfaces Physical layer security Security

来源：评论

学校读者我要写书评

暂无评论

Horizontal-to-vertical video conversion

arXiv

引用

arXiv 2021年

作者： Zhu, Tun Zhang, Daoxin Hu, Yao Wang, Tianran Jiang, Xiaolong Zhu, Jianke Li, Jiawei The College of Computer Science Zhejiang University Hangzhou310027 China The Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Company of Alibaba Youku Cognitive and Intelligent Lab

At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is hereby tackled by our automated horizontal-to-vertical (abbreviated as H2V) video conversion framework. Essentially, the H2V framework performs subject-preserving video cropping instantiated in the proposed Rank-SS module. Rank-SS incorporates object detection to discover the candidate subjects, from which we select the primary subject-to-preserve leveraging location, appearance, and salient cues in a convolutional neural network. In addition to converting horizontal videos vertically by cropping around the selected subject, automatic shot detection and multi-object tracking are integrated into the H2V framework to accommodate long and complex videos. To develop H2V systems, we collect an H2V-142K dataset containing 125 videos (132K frames) and 9,500 cover images annotated with primary subject bounding boxes. On H2V-142K and public object detection datasets, our method demonstrates promising results on the subject selection comparing to the related solutions. Furthermore, our H2V framework is industrially deployed hosting millions of daily active users and exhibits favorable H2V conversion performance. By making this dataset as well as our approach publicly availab.e, we wish to pave the way for more horizontal-to-vertical video conversion research. Our collected H2V-142K dataset is availab.e at H2V-142K website. © 2021, CC BY.

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

arXiv

引用

arXiv 2024年

作者： Shi, Hao Wang, Song Zhang, Jiaming Yin, Xiaoting Wang, Zhongdao Wang, Guangming Zhu, Jianke Yang, Kailun Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Huawei Noah’s Ark Lab. Canada The Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining lab.ls using explicit multi-view geometry and integrating sensor bias, especially to increase the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner elevates vision-based SSC models to a level even surpassing that of LiDAR-based onboard SSC models. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly availab.e at https://***/MasterHow/OccFiner. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Forearm Muscle Synergy Reducing Dimension of the Feature Matrix in Hand Gesture Recognition 3

Forearm Muscle Synergy Reducing Dimension of the Feature Mat...

引用

3rd IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2018

作者： Luo, Xiu Y. Zhang, Yao Zhao, Yun Hu, Lin T. Yang, Dan D. Wu, Xiao Y. Chen, Lin Hu, Ning Hou, Wen S. Key Lab. of Biorheological Sci. and Technol. of Min. of Educ. Collab. Innov. Ctr. for Brain Science Chongqing University Chongqing China Chongqing Medical Electronics Engineering Technology Research Center Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing China

ISBN: (纸本)9781538670668

robotic hand exoskeleton has been proposed to train the paretic hand after stroke, and its movements can be controlled by recognized activities of non-paretic hand. Although a variety of myoelectric pattern identification strategies have been proposed to distinguish different hand gestures by the surface electromyogram (sEMG) signals, the feature matrix of hand movements should be simplified for on-line myoelectric control. Synergistic muscle activity has been known as a principle for center nervous system (CNS) to modulate the multi-joint limb movement, here, we aim to study the feasibility of the muscle synergies for gesture recognition with simplified feature vector. Five healthy participants were recruited to perform hand tasks of hand open(open), hand close(close), key pinch(key), palm valgus(val), and grasp cylindrical tool(cyl). sEMG were recorded from six forearm hand muscles, and non-negative matrix factorization (NMF) was used to extract the synergistic patterns of myoelectrical activities. The preliminary results showed that each hand gesture can be expressed with one muscle synergy pattern, which consists of the contribution of six forearm muscles. With the synergistic features, the recognition rate of hand gestures reached 96.08%. The present work suggested that the muscle synergy can be used for hand gesture recognition with low dimension feature vector. © 2018 IEEE.

关键词： Gesture recognition

来源：评论

学校读者我要写书评

暂无评论

Domo: Passive Per-Packet Delay Tomography in Wireless Ad-hoc Networks

Domo: Passive Per-Packet Delay Tomography in Wireless Ad-hoc...

引用

International Conference on Distributed Computing Systems

作者： Yi Gao Wei Dong Chun Chen Jiajun Bu Tianyu Chen Mingyuan Xia Xue Liu Xianghua Xu Zhejiang University Hangzhou Zhejiang CN Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University China Hangzhou Dianzi University Hangzhou Zhejiang CN School of Computer Science McGill University Canada Zhejiang Provincial Key Lab of Data Storage and Transmission Technology Hangrhou Dianzi University China

In multi-hop wireless ad-hoc networks, packet delivery delay is one of the most important performance metrics. While a lot of research efforts have been spent on measuring and optimizing the end-to-end delay performance, there usually lack accurate and lightweight methods for decomposing the end-to-end delay into the per-hop delay for each packet. Knowledge on the per-hop per-packet delay can greatly improve the network visibility and facilitate network measurement and management. In this paper, we propose Domo, a passive, lightweight and accurate delay tomography approach to decomposing the packet end-to-end delay into each hop. The basic idea is to formulate the problem into a set of optimization problems by carefully considering the constraints among various timing quantities. At the network side, Domo attaches a small overhead to each packet for constructing constraints of the optimization problems. At the PC side, Domo employs semi-definite relaxation and several other methods to efficiently solve the optimization problems. We implement Domo and evaluate its performance extensively using large-scale simulations. Results show that Domo significantly outperforms two existing methods, nearly tripling the accuracy of the state-of-the-art.

关键词： Delays Ad hoc networks Wireless communication Optimization Accuracy Tomography Educational institutions

来源：评论

学校读者我要写书评

暂无评论

Harmonious Hashing

Harmonious Hashing

引用

23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Xu, Bin Bu, Jiajun Lin, Yue Chen, Chun He, Xiaofei Cai, Deng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China State Key Lab of CAD and CG College of Computer Science Zhejiang University China

ISBN: (纸本)9781577356332

Hashing-based fast nearest neighbor search technique has attracted great attention in both research and industry areas recently. Many existing hashing approaches encode data with projection-based hash functions and represent each projected dimension by 1-bit. However, the dimensions with high variance hold large energy or information of data but treated equivalently as dimensions with low variance, which leads to a serious information loss. In this paper, we introduce a novel hashing algorithm called Harmonious Hashing which aims at learning hash functions with low information loss. Specifically, we learn a set of optimized projections to preserve the maximum cumulative energy and meet the constraint of equivalent variance on each dimension as much as possible. In this way, we could minimize the information loss after binarization. Despite the extreme simplicity, our method outperforms superiorly to many state-of-the-art hashing methods in large-scale and high-dimensional nearest neighbor search experiments.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：