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IEEE International Conference on Image Processing

作者： Jamil Ahmad Wail Gueaieb Abdulmotaleb El Saddik Giulia De Masi Fakhri Karray Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE School of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Domain knowledge exists in various forms, including text, ontologies, graphs, images, audio, and videos. In plant disease detection, most works solely utilize images with disease labels, neglecting textual descriptions of visual disease symptoms used by human experts for diagnosis. These text descriptions and sample images aid expert identification of visual symptoms. We propose a novel method that leverages text descriptions and image data by modeling domain-specific knowledge about visual symptoms in leaf images as separate feature channels. Each channel corresponds to specific features whose absence or presence in the image influences model predictions. We introduce a channel attention-guided fusion module for weighting each channel based on the input and corresponding output. The combined feature channels are transformed into a standardized 3-channel input format, which can then be processed by any pre-trained convolutional neural network (CNN) as input for feature extraction and subsequent classification. Furthermore, intermediate activations of the channel attention layer combined with the weights from the fusion layer make model predictions explainable. Experimental results on three publicly available datasets of apple and cucumber leaf diseases demonstrate improvements of up to 5% utilizing various state-of-the-art CNN architectures, indicating the efficacy of incorporating textual disease descriptions using the proposed approach.

关键词： Training Visualization Plant diseases Predictive models Feature extraction Data models Robustness

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Internet of Things Enabled Automatic Meter Reading

Internet of Things Enabled Automatic Meter Reading

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Smart Devices (ICSD), International Conference on

作者： Sarthak Malik Shikha Agarwal Srinivas Aluvala Jyoti S. Bali Rachit Garg Mohemmed Hussien School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Ajay Kumar Garg Engineering College Ghaziabad UP India School of Computer Science & Artificial Intelligence SR University Warangal Dept of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune The Islamic University Najaf Iraq

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

The world is currently going through a huge transition towards digitalization and sustainability. Sustainability focuses on responsible resource management and environmental preservation, whereas digitalization entails the conversion of many processes and systems into connected, digital forms. Energy management, which aims to offer clean and affordable energy solutions while minimising waste and environmental damage, is one of the important sectors garnering significant attention in this context. Energy management involves the methodical optimisation of energy use in a variety of contexts, including commercial and industrial facilities and residential settings. The energy metre, a tool used to measure electricity, gas, or other energy kinds, is essential to this project. Traditional energy metres are evolving into "smart" energy metres, which may send consumption data periodically or in real-time in addition to measuring energy usage. The usability of energy metres is considerably increased by this feature for both consumers and energy suppliers. Industry 4.0 refers to the fourth industrial revolution, which is characterised by the incorporation of digital technologies into manufacturing and industrial processes, in the broader context of business and technology. Internet of Things (IoT), artificial intelligence (AI), and automation are key technologies advancing this transformation. These technologies are essential for improving productivity, sustainability, and efficiency across sectors. A key component of increasing energy management practises is the Internet of Things (IoT). The internet of things (IoT) is a network of connected gadgets and sensors that can gather and send data. IoT-enabled devices are crucial to the real-time monitoring and management of energy use in the field of energy management. These gadgets are capable of collecting information from many sources, such as energy metres, and transmitting it for analysis and well-informed decision-making. An

关键词： Cloud computing Accuracy Electricity Sensor phenomena and characterization Real-time systems Internet of Things Servers Energy management Sustainable development Monitoring

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Parameter Efficient Merging for Multimodal Large Language Models with Complementary Parameter Adaptation

arXiv

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arXiv 2025年

作者： Zeng, Fanhu Guo, Haiyang Zhu, Fei Shen, Li Tang, Hao State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong School of Cyber Science and Technology Sun Yat-sen University China School of Computer Science Peking University China

Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks. Merging models into one universal model to empower multi-task ability refraining from data leakage has gained popularity. With the expansion in data and model size, parameter efficient tuning becomes the common practice for obtaining task-specific models efficiently. However, we observe that existing methods designed for full fine-tuning merging fail under efficient tuning. To address the issues, we analyze from low-rank decomposition and reveal that maintaining direction and compensating for gap between singular values are crucial for efficient model merging. Consequently, we propose CoPA-Merging, a training-free parameter efficient merging method with complementary parameter adaptation. Specifically, we (1) prune parameters and construct scaling coefficients from inter-parameter relation to compensate for performance drop from task interference and (2) perform cross-task normalization to enhance unseen task generalization. We establish a benchmark consisting of diverse multimodal tasks, on which we conduct experiments to certificate the outstanding performance and generalizability of our method. Additional study and extensive analyses further showcase the effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Information leakage

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Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning

Incentivizing Participation in SplitFed Learning: Convergenc...

引用

International Conference on Distributed Computing Systems

作者： Pengchao Han Chao Huang Xingyan Shi Jianwei Huang Xin Liu School of Information Engineering Guangdong University of Technology Guangzhou China Department of Computer Science The University of California Davis USA School of Data Science The Chinese University of Hong Kong Shenzhen China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In SplitFed learning (SFL), a global model is split into two segments, where distributed clients train the first segment in a federated manner and a main server trains the other. Existing studies focus on algorithm development but ignore the important issue of incentives, without which self-interested clients may be unwilling to participate. We fill this gap by presenting a first incentive study in SFL. One challenge is that the design requires an understanding of how clients' participation affects the model performance. To this end, we provide a first convergence analysis for SFL considering partial client participation to guide the mechanism design. Another challenge is that monetary payment may not be viable for large distributed systems. To this end, we propose a model-versioning mechanism where the main server assigns different versions of models (of different qualities) to clients as incentives. The design is further complicated by clients' multi-dimensional private information. To this end, we design the model-versioning mechanism so that it decouples clients' decisions and admits a weakly dominant strategy at equilibrium. We prove that our mechanism is feasible, effective, and incentive compatible. Experimental results show that our mechanism greatly improves client participation and model accuracy compared to a benchmark.

关键词： Analytical models Accuracy Mechanism design Benchmark testing Numerical models Servers Distributed computing

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Optimized Admittance Control for Manipulators Interacting with Unknown Environment 25

Optimized Admittance Control for Manipulators Interacting wi...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Kong, Haiyi Peng, Guangzhu Li, Guang Yang, Chenguang University Of Manchester Department Of Electrical And Electronic Engineering Oxford Rd ManchesterM13 9PL United Kingdom University Of The West Of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom School Of Automation Nanjing University Of Information Science And Technology Nanjing210044 China University Of Liverpool Department Of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350340266

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Manipulators

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A Joint Learning of Force Feedback of Robotic Manipulation and Textual Cues for Granular Materials Classification

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IEEE robotics and Automation Letters 2025年第7期10卷 7166-7173页

作者： Zeqing Zhang Guanqi Chen Wentao Chen Ruixing Jia Guanhua Chen Liangjun Zhang Jia Pan Peng Zhou Department of Computer Science The University of Hong Kong Hong Kong Centre for Garment Production Limited Hong Kong Hong Kong Southern University of Science and Technology Shenzhen China Robotics and Autonomous Driving Lab Baidu Research Mountain View CA USA School of Advanced Engineering The Great Bay University Dongguan China

Granular materials (GMs) are formed by a collection of particles. Even if their visual representation is straightforward, it can be seriously affected in the visually constrained environment. Based on frequency features observed in force signals, this paper proposes a non-visual classifier, GmClass, using the force feedback in the robot-granules interaction. Specifically, we transform the force sequences into the frequency domain and integrate them with high-dimensional textual information into a two-branch architecture for multimodal supervised contrastive learning (MSCL). This method achieves an 84.10% classification accuracy, surpassing traditional supervised learning by 10% and outperforming supervised contrastive learning by more than 40%, demonstrating the positive impact of adding text modality on classification, and when applied to a larger dataset, it attains an even higher 85.28% accuracy, further validating its effectiveness. Also, we demonstrate the performance of our approach in handling unseen particles and the generalization capability for varying data collection parameters.

关键词： Force Feature extraction Robots Probes Frequency-domain analysis Force feedback Contrastive learning Transforms Solids Frequency conversion

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Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning

arXiv

引用

arXiv 2025年

作者： Guo, Qianyu Wu, Jingrong Wu, Tianxing Wang, Haofen Ge, Weifeng Zhang, Wenqiang School of Computer Science Fudan University China Shanghai Institute of Virology Shanghai Jiao Tong University School of Medicine China School of Computer Science and Engineering Southeast University Nanjing210096 China College of Design and Innovation Tongji University China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai20043 China

Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In real-world scenarios, environmental factors such as complex backgrounds, varying lighting conditions, long-distance shooting, and moving targets often cause test images to exhibit numerous incomplete targets or noise disruptions. However, current research on evaluation datasets and methodologies has largely ignored the concept of "environmental robustness", which refers to maintaining consistent performance in complex and diverse physical environments. This neglect has led to a notable decline in the performance of FSL models during practical testing compared to their training performance. To bridge this gap, we introduce a new real-world multi-domain few-shot learning (RD-FSL) benchmark, which includes four domains and six evaluation datasets. The test images in this benchmark feature various challenging elements, such as camouflaged objects, small targets, and blurriness. Our evaluation experiments reveal that existing methods struggle to utilize training images effectively to generate accurate feature representations for challenging test images. To address this problem, we propose a novel conditional representation learning network (CRLNet) that integrates the interactions between training and testing images as conditional information in their respective representation processes. The main goal is to reduce intra-class variance or enhance inter-class variance at the feature representation level. Finally, comparative experiments reveal that CRLNet surpasses the current state-of-the-art methods, achieving performance improvements ranging from 6.83% to 16.98% across diverse settings and backbones. The source code and dataset are available at https://***/guoqianyualberta/Conditional-Representation-Learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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The SGM Algorithm based on Census Transform for Binocular Stereo Vision

The SGM Algorithm based on Census Transform for Binocular St...

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2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022

作者： Zhang, Liye Cai, Fudong Wang, Jinjun Lv, Changfeng Liu, Wei Guo, Guoxin Liu, Huanyun Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Electric Power Automation Division Shandong Senter Electronic Zibo China School of Computer Science and Technology Shandong University of Technology Zibo China Platform Algorithm Department Jinan Xintongda Electronic Technology Co. Ltd Jinan China

ISBN: (纸本)9781665495677

Aiming to reduce the error matching rate of binocular image matching and improve the accuracy of disparity map, the matching cost calculation method of Semi-Global Matching (SGM) algorithm is improved, the Census transform and adaptive window is used to calculate the cost simultaneously, and the size and shape of the cost calculation window are determined by judging the gradient size of matching points, which improves the matching accuracy and reduces the computational complexity of the algorithm. Experimental results show that the proposed algorithm can effectively improve the accuracy of binocular ranging. © 2022 IEEE.

关键词： Image matching

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Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

arXiv

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arXiv 2024年

作者： Lin, Junxiong Tao, Zen Tong, Xuan Mai, Xinji Wang, Haoran Wang, Boyang Wang, Yan Zhao, Qing Yu, Jiawen Lin, Yuxuan Yan, Shaoqi Gao, Shuyong Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China East China University of Science and Technology Shanghai China Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an Uncertainty-based degradation representation for blind Super-Resolution framework (USR). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. Rhe VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Solution for Agri Disaster Using Climate Changes Adaptation by Ml 6

Solution for Agri Disaster Using Climate Changes Adaptation ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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