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共 18111 条记录，以下是291-300 订阅

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KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission for Visual Classification

KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Tra...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yue Zhu Haiwen Diao Shang Gao Long Chen Huchuan Lu Dalian University of Technology Dalian China The Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fine-tuning pre-trained vision models for specific tasks is a common practice in computer vision. However, this process becomes more expensive and resource-intensive as models grow larger. Recently, parameter-efficient fine-tuning (PEFT) methods have emerged as a popular solution to improve training efficiency and reduce storage needs by tuning additional low-rank modules within pre-trained backbones. Despite their advantages, they struggle with limited representation capabilities and misalignment with pre-trained intermediate features. To address these issues, we introduce an innovative Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission (KARST) for various recognition tasks. Specifically, KARST’s multi-kernel design extends Kronecker projections horizontally and separates adaptation matrices into multiple complementary spaces, reducing parameter dependency and creating more compact subspaces. Besides, it incorporates extra learnable re-scaling factors to better align with pre-trained feature distributions, allowing for more flexible and balanced feature aggregation. Extensive experiments on diverse downstream datasets validate that our KARST not only outperforms other PEFT counterparts across model types and data domains, but also surpasses full fine-tuning with a negligible inference cost due to its re-parameterization characteristics.

关键词： Training Adaptation models Visualization Computational modeling signal processing Performance gain Data models Speech processing Kernel Tuning

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce

Artificial Intelligence Algorithms for Robotic Harvesting of...

引用

AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Nilesh R. Kolhalkar Anupama A. Pandit Shridhar Ashok Kedar G. Yedukondalu Department of Mechanical Engineering MKSSS's Cummins College of Engg. For Women Pune MH India Department of Computer Science & Engg. Defence Institute of Advanced Technology (D.I.A.T) Pune Pune MH India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada A.P. India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations.

关键词： Accuracy Costs Automation Machine vision Pipelines Lighting Training data robot sensing systems Prediction algorithms robots

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WSe2-Based Bi-Metallic SPR Refractive Index Sensor with Enhanced Sensing Properties

WSe2-Based Bi-Metallic SPR Refractive Index Sensor with Enha...

引用

international conference on robotics,Electrical and signal processing Techniques (ICREST)

作者： Biswajit Dey Md Tawabur Rahman Department of Electrical and Electronic Engineering Khulna University of Engineering &Technology Khulna Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

The surface plasmon resonance (SPR) method is a novel and precise measurement approach for sensing applications. Here, a tungsten diselenide (WSe2) based bimetallic SPR refractive index (RI) sensor has been modeled and designed utilizingLumerical Finite Difference Time Domain (FDTD) platform. The angular interrogation method has been employed to generate SPR response by varying refractive index (RI) of analyte and it is captured by the Discrete Fourier Transform (DFT) monitor. The sensor exhibits an enhanced sensitivity of 210 deg/Refractive Index Unit (RIU) with quality factor and detection accuracy of 225.80/RIU and 1.13 respectively. The sensor showed a broad sensing range for RI ranging between 1.33 to 1.36. Furthermore, the distribution of electric field is analyzed for the developed structure and the peak intensity is found at WSe2/analyte interface having a penetration depth of about 122 nm. Thus, the sensor could be utilized in the field of healthcare and environmental monitoring.

关键词： Sensitivity Discrete Fourier transforms Refractive index Tungsten signal processing robot sensing systems Surface plasmon resonance Sensors Time-domain analysis Finite difference methods

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Less is more: Efficient Scene Graph Generation with reparameterization

Less is more: Efficient Scene Graph Generation with reparame...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jonghwan Hong Seonghyeok Noh Bonhwa Ku Hanseok Ko Vision Information Processing Engineering Korea University Seoul Korea Electrical Engineering Korea University Seoul Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Scene Graph Generation (SGG) aims to identify objects and their relationships in visual scenes but faces two key challenges: high computational overhead, particularly for real-time applications, and the long-tailed distribution of predicates, which biases models toward frequent relationships. To address these challenges, we propose Reparams-SGG, a lightweight and efficient network architecture composed of multi-path residual blocks. This architecture reduces computational overhead by leveraging a reparameterization strategy that minimizes sequential and parallel processing, making it highly efficient during inference. Moreover, we introduce a dynamic focal loss that dynamically adjusts the temperature scale during training to focus learning on rare predicates, promoting progressively unbiased learning. Additionally, we propose a dynamic distribution loss, compensating for learning limitations solely from one-hot distributions under data imbalance conditions. We evaluate our method on the widely-used Visual Genome and the recent PSG dataset. Reparams-SGG achieves competitive performance with significantly fewer parameters than state-of-the-art models, demonstrating its efficiency and suitability for deployment in resource-constrained environments.

关键词： Training Visualization Heavily-tailed distribution Genomics signal processing Parallel processing Network architecture Real-time systems Object recognition Speech processing

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HiLiteMamba: A Lightweight and High-Frequency Aware Network for Single Image Super-Resolution

HiLiteMamba: A Lightweight and High-Frequency Aware Network ...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zijing Zhang Jianfei Xiao Bate Liu Beijing University of Posts and Telecommunications Beijing China University of Southern California California United States of America

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transformer has widely been applied in various low-vision tasks, achieving significant strides in single image super-resolution. However, its low-pass characteristic still limits the ability of Transformer-based models to represent rich texture details in images. Additionally, the quadratic computational complexity of the attention mechanism also restricts its application in low-level tasks. In these regards, we propose a novel High-frequency awareness and Lightweight Mamba network (HiLiteMamba). It combines a lightweight Mamba module with a convolution block to model different features complementarily. Moreover, we develop the High-Freq Highway with residual connection to maintain deep propagation of high-frequency information and accelerate its modeling. Extensive experiments demonstrate that HiLiteMamba achieves state-of-the-art performance among baseline models.

关键词： Road transportation Filters Convolution Computational modeling Superresolution Transformers Robustness Computational complexity Speech processing signal resolution

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Efficient Large-Scale Scene Point Cloud Upsampling with Implicit Neural Networks and Spatial Hashing

Efficient Large-Scale Scene Point Cloud Upsampling with Impl...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhiyong Zhang Yunrui Zhu Ruyu Liu Xu Cheng Jianhua Zhang Xiufeng Liu School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point cloud upsampling is a critical challenge in 3D vision, particularly for large-scale, real-world data. We propose ASFNet, a novel implicit neural network-based approach that uniquely combines adaptive spatial feature representation with efficient spatial hashing. This method significantly improves both upsampling quality and computational efficiency. ASFNet first encodes the point cloud as an implicit surface, employing dynamic search and spatial hashing to optimize query point locations rapidly. This approach creates a uniform, continuous field around surfaces, enabling high-fidelity upsampling. Experiments on benchmark datasets, including Oakland 3D dataset and VMR-Oakland-v2, demonstrate ASFNet’s superiority. Our method achieves state-of-the-art performance with a Chamfer Distance of 5.559 × 10 –3 on Oakland 3D dataset, while reducing processing time by up to 80% compared to existing methods. On the challenging Oakland 3D dataset, ASFNet completes upsampling in just 150 seconds. These results underscore ASFNet’s potential to advance real-time 3D vision applications in areas such as autonomous navigation and augmented reality.

关键词： Point cloud compression Technological innovation Solid modeling Three-dimensional displays Neural networks signal processing Neural radiance field Real-time systems Speech processing Surface treatment

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Anomaly Segmentation of Leather Defects using Modified U-Net Architectures

Anomaly Segmentation of Leather Defects using Modified U-Net...

引用

international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： J Suriya Prakash Yakub Bhanothu Academy of Scientific & Innovative Research (AcSIR) CSIR-Central Electronics Engineering Research Institute India CSIR-CEERI Chennai Centre CSIR-Central Electronics Engineering Research Institute India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

An automated real-time leather inspection system based on machine vision needs defect detection algorithms that are accurate and fast to aid the requirements of the leather industry. Precise indication of defective areas on the leather needs to be highly accurate for two reasons: to avoid defective regions in final graded leather and to eliminate wastage of defectless regions. In this work, we have proposed two modified U-Net architectures to reduce the number of learning parameters without affecting the segmentation accuracy. These models were compared with state-of-the-art segmentation architectures, namely U-Net (with VGG16/ResNet34 encoders) and YOLO11. The models were evaluated based on performance metrics, which found that the proposed models provide results comparable to the classical U-Net and YOLO11 segmentation architectures.

关键词： Training Wireless communication Measurement Accuracy Semantic segmentation Object segmentation Inspection signal processing Real-time systems Convolutional neural networks

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Improving the Stability in Intensity and Absorbance of an Optical Fiber CO2 Sensor

Improving the Stability in Intensity and Absorbance of an Op...

引用

international conference on robotics,Electrical and signal processing Techniques (ICREST)

作者： Nadia Afroze Serhiy Korposh Ricardo Correia Barrie R. Hayes-Gill Seung-Woo Lee Stephen P. Morgan Optics and Photonics Research Group University of Nottingham Nottingham UK Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Chemical and Environmental Engineering The University of Kitakyushu Kitakyushu Japan

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Achieving the stable intensity response of an optical fiber CO 2 sensor (OFCS) is challenging due to film cracking and humidity. In this work, two OFCSs (OFCS 1 and 2) were fabricated without and with Triton X-100, and the differences in stability were observed after storing these for 35 days. Also, the stability of an OFCS was monitored without and with polytetrafluoroethylene (PTFE) cast film. The absorbance at 600 nm decreases from 0.41 to 0.18 for OFCS 1 without Triton X-100, whereas the absorbance at 600 nm fluctuates between 0.82 and 0.84 for OFCS 2 with Triton X-100 within a storage period (9–35 days). The reflection intensity at 600 nm reduces by 12.4 % and only 0.82 % for an OFCS without PTFE film and with PTFE film, respectively, for the relative humidity increment between 50 % and 70 %. Triton X-100 and PTFE film successfully showed the absorbance and reflection intensity reduction at 600 nm for the OFCS.

关键词： Humidity Medical services signal processing robot sensing systems Reflection Skin Monitoring

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SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis

SHAP-Integrated Convolutional Diagnostic Networks for Featur...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yan Hu Ahmad Chaddad Laboratory for AIPM School of Artificial Intelligence Guilin University of Electronic Technology China The Laboratory for Imagery Vision and Artificial Intelligence École de Technologie Supérieure Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited datasets, to address the challenge posed by data privacy regulations that restrict access to medical datasets. The SICDN model was tested on classification tasks using pneumonia and breast cancer datasets, demonstrating over 97% accuracy and surpassing four popular CNN models. We also integrated a historical weighted moving average technique to enhance feature selection. The SICDN shows potential in medical image prediction, with the code available on https://***/AIPMLab/SICDN.

关键词： Convolutional codes Data privacy Pneumonia Convolution Feature extraction Regulation Data models Convolutional neural networks Speech processing Medical diagnostic imaging

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Uncertainty-aware Correspondence Distillation for Deep Image Clustering

Uncertainty-aware Correspondence Distillation for Deep Image...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Luyao Chang Leiting Chen Chuan Zhou School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Image clustering is a challenging task in computer vision, with performance heavily dependent on the quality of feature representations due to the inherent complexity of images. However, current image clustering methods overlook the underlying semantic information during representation learning, leading to low-quality feature representations. Moreover, the absence of ground-truth labels amplifies the detrimental effects of unreliable data on semantic guidance, steering the model towards incorrect learning directions. In this work, we propose a novel deep image clustering method named Uncertainty-aware Correspondence Distillation (UCD) to address these issues. Specifically, we introduce the concept of representation correspondence to establish cross-level connections between instances and semantics, which is further employed as a distillation target to improve the network’s feature learning by complementing semantic information. To mitigate unnecessary similarity penalties arising from unreliable data, we develop robust dynamic weights for semantic guidance by modeling the uncertainty of image semantics. Extensive experiments on five benchmark datasets demonstrate the superiority of the proposed method. The code is available at https://***/YL616/UCD.

关键词： Representation learning Computer vision Uncertainty Clustering methods Semantics Noise Interference Data models Complexity theory Speech processing

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