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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Symmetric Graph Contrastive Learning against Noisy Views for Recommendation

arXiv

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arXiv 2024年

作者： Zhao, Chu Yang, Enneng Liang, Yuliang Zhao, Jianzhe Guo, Guibing Wang, Xingwei Software College Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods, such as directly perturbing interaction graph (e.g., node/edge dropout), may interfere with the original connections and generate poor contrasting views, resulting in sub-optimal performance. In this paper, we define the views that share only a small amount of information with the original graph due to poor data augmentation as noisy views (i.e., the last 20% of the views with a cosine similarity value less than 0.1 to the original view). We demonstrate through detailed experiments that noisy views will significantly degrade recommendation performance. Further, we propose a model-agnostic Symmetric Graph Contrastive Learning (SGCL) method with theoretical guarantees to address this issue. Specifically, we introduce symmetry theory into graph contrastive learning, based on which we propose a symmetric form and contrast loss resistant to noisy interference. We provide theoretical proof that our proposed SGCL method has a high tolerance to noisy views. Further demonstration is given by conducting extensive experiments on three real-world datasets. The experimental results demonstrate that our approach substantially increases recommendation accuracy, with relative improvements reaching as high as 12.25% over nine other competing models. These results highlight the efficacy of our method. The code is available at https://***/user683/SGCL. © 2024, CC BY.

关键词： Contrastive Learning

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Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies

Development of Classification Model based on Arabic Textual ...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Hanen Tarik Himdi Fatmah Yousef Assiri Computer Science and Artificial Intelligence Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Software Engineering Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The ease of communication that has been made possible by chat messaging platforms, such as WhatsApp and Line, and their increased use and ubiquity in society, have motivated purveyors of fake news to create and present their news as legitimate. Though different organisations continued their efforts to resolve this problem, many of the developed solutions rely on verifying the associated metadata, which are not always available. Several studies have attempted to identify fake news by analysing textual content; however, there is a lack of studies on Arabic language sources. Our work filled this gap; we proposed a machine learning model that classifies Arabic fake news based on textual analysis. This paper shows the feasibility of our approach to classify Arabic text in social media applications.

关键词： Analytical models Technological innovation Freeware Social networking (online) Smart cities Machine learning Metadata

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Significance of State-of-Art Search Engine in Game Development 10

Significance of State-of-Art Search Engine in Game Developme...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Goyal, Mayank Kumar Ravulakollu, Kiran Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Video game development process is different from other software projects while yet being related to them in software engineering. It is distinctive in that it incorporates the work of teams from several fields (art, music, acting, programming, etc.), and that it seeks to create a compelling gaming experience by using prototypes and iterations. This research study focuses on game development and how it is accomplished utilizing a game engine. The process of creating/developing games from a concept is known as game development. Game engines are software programs that help game developers create games. The literature study discusses about the various game engine. The utility of the 'Unity' gaming engine is investigated in this paper. Unity may be utilized in different fields as study subjects, but there can be certain frequent benefits & obstacles of utilizing it in different case studies. Students learn which game engine to use based on their specific requirements. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Search engines

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An Ultra-wideband Wide-beam Magneto-electric Dipole Antenna For Target Detection

An Ultra-wideband Wide-beam Magneto-electric Dipole Antenna ...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Zhitao Ye Wanchen Yang Dongxu Chen Haonan Tian Li Wei College of Computer Science and Software Engineering Hohai University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

This manuscript presents an ultra-wideband magneto-electric (ME) dipole antenna that owns wide half-power beamwidth (HPBW) in both the E- and H-plane. Firstly, by adopting two $\Pi$-shaped parasitic patches, the antenna features wider HPBW in both the E- and H-plane. Meanwhile, by selecting a pair of $\Gamma$-shaped differentially-fed probes, the impedance bandwidth of the antenna is obviously widened, moreover, it also effectively improve the pattern skewness in the E-plane. Furthermore, by using larger square patches, the impedance bandwidth of the antenna is further broadened. The results indicate that the antenna has a small footprint of $0.24 \lambda_{0} \times 0.32 \lambda_{0}$ (@8.8GHz), a very large $10-\mathrm{dB}$ impedance bandwidth of $81 \%$ (from 8.8 to 20.8 Hz), and very wide HPBW of $103^{\circ}-154^{\circ}$ and 81° 108° for the H -plane and E-plane from 9 GHz to 16 GHz. Due to its characteristics of ultra-wide band, wide beam, and small size, it has promising prospects for development in the field of target detection.

关键词： Microwave antennas Dipole antennas Magnetoelectric effects Bandwidth Object detection Terahertz radiation Ultra wideband antennas Impedance Probes Antenna radiation patterns

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Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Yang Chen, Yanjiao Xiong, Ping Chen, Fei Chen, Jing Zhongnan University of Economics and Law School of Information Engineering Wuhan China Zhejiang University College of Electrical Engineering Hangzhou China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Wuhan University School of Cyber Science and Engineering Wuhan China

As cloud storage becomes popular, more and more users tend to outsource their data to powerful cloud severs. To prevent a single point of failure, users prefer to store data on multiple cloud servers from different cloud service providers. However, after outsourcing data to cloud servers, users lose the control of their data, which may incur many serious security issues, such as abnormal data tampering and deleting. It is necessary for users to audit multiple cloud storage aperiodically. In this paper, we aim to design a decentralized self-auditing solution for multiple cloud storage, in which cloud servers can audit the integrity of each other, and thus no third-party entity is required. Firstly, based on basic algebra, our protocol realizes decentralized self-auditing multiple cloud storage that only involves encrypted user data. Secondly, we design a proof exchanging mechanism, making any number of cloud servers form the same final integrity proof with a linear number of interactions. Thirdly, our solution can achieve cloud dynamics, which can freely enable and disable a cloud server to provide storage service. At last, security proof and performance evaluation show that the proposed protocol has provable security and high efficiency. © 2004-2012 IEEE.

关键词： Cloud security

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Enhancing Sales Forecasting Accuracy in the Presence of Missing Data

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Jun Tang Jingpeng Sun Bing Guo Yan Shen Shengxin Dai Peng Wang College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai UAE School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. The proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios.

关键词： Interpolation Accuracy Computational modeling Predictive models Network architecture Feature extraction Data models Stability analysis Convolutional neural networks Forecasting

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Mutual Graph Learning Network and Diffusion Probabilistic Model-based Medical Image Segmentation

Mutual Graph Learning Network and Diffusion Probabilistic Mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Su, Huaqiang Lei, Haijun Chen, Guoliang Chen, Xin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Guangzhou First People's Hospital Department of Radiology Guangzhou China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China

ISBN: (纸本)9798350337488

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network's ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16. © 2023 IEEE.

关键词： Diffusion probability model feature fusion module medical segmentation mutual graph learning network

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Research on Inverted Pendulum Control Strategy Based on Deep Reinforcement Learning 3

Research on Inverted Pendulum Control Strategy Based on Deep...

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3rd International Conference on Electrical engineering and Control science, IC2ECS 2023

作者： Zheng, Jiehong Wang, Xinzui Yang, Yanli Jihua Medical Institute Jihua Laboratory Foshan528251 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou215163 China

ISBN: (纸本)9798350382426

Aiming at the control planning of inverted pendulum task, the planning strategy based on Soft Actor-Critic (SAC) algorithm was studied. An agent based on Actor-Critic framework is designed, which takes inverted pendulum state as input and planned target position as output as controller. The agent has a total of 5 neural networks, namely, action network actor, 2 current evaluation networks and 2 target evaluation networks. Actor network outputs the next planned position according to the current inverted pendulum state, and the current Critic network and target Critic network respectively output the value evaluation of the current action and the next action according to the reward feedback from the environment. The PD control method is used to convert the planned target position of the mobile network output into the control voltage value, and actually control the inverted swing. Simulation results show that the proposed method is effective and efficient. © 2023 IEEE.

关键词： deep reinforcement learning motion control neural network rotary inverted pendulum Soft Actor-Critic algorithm

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ao Song Zhou Lei Shengbo Chen College of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments

关键词： Semantics Training data Production Learning (artificial intelligence) Knowledge representation Information filters Data models Internet Information technology Recommender systems

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