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共 18110 条记录，以下是271-280 订阅

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HiE-VL: A Large vision-Language Model with Hierarchical Adapter for Handwritten Mathematical Expression Recognition

HiE-VL: A Large Vision-Language Model with Hierarchical Adap...

引用

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

作者： Hong-Yu Guo Fei Yin Jian Xu Cheng-Lin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large vision-Language Models (LVLMs) have shown impressive capabilities across various domains, but existing LVLMs have limited performance in dense perception and structured learning problems, such as Handwritten Mathematical Expression Recognition (HMER). The primary challenges stem from the complexity of formula images comprising multiple symbols and complicated inter-symbol relationships. This poses difficulties to LVLMs with locality insensitive visual encoders and structure-agnostic vision-language projectors. To overcome these challenges, we propose HiE-VL, the first LVLM for HMER containing: (1) a primitive-aware high-resolution visual encoder, (2) a hierarchical adapter, (3) a math-context enhanced large language model (LLM). Specifically, the adopted visual encoder allows locating and recognizing symbols in complex formula images. The hierarchical adapter functions as a vision-language projector to progressively capture primitive and structure information for facilitating expression decoding. The whole model is optimized in a two-stage training pipeline. In experiments on two benchmark datasets of HMER, our model achieves significantly higher performance than existing LVLMs like GPT-4V and state-of-the-art HMER models. Our codes are available at https://***/guohy17/HiE-VL.

关键词： Training Adaptation models Visualization Handwriting recognition Symbols Speech recognition Benchmark testing Mathematical models Speech processing Tuning

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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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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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Analysis of Fringe Visibility and Film Thickness Relationship of an Optical Fiber CO2 Sensor

Analysis of Fringe Visibility and Film Thickness Relationshi...

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

The thickness of a Fabry-Perot interferometer (FPI) cavity plays a vital role controlling the intensity, absorbance, and fringe visibility of the FPI at a particular wavelength. In this work, an FPI-based optical fiber CO 2 sensor (OFCS) was simulated for the film thickness range of 4.88-7.23 μm (achieved from a few scanning electron microscope images of an OFCS). The absorbance peak was achieved at a wavelength of 600 nm. The normalized intensity decreases by 86.7 %, absorbance increases by 35.1 %, and fringe visibility decreases by 3.31 % at around 600 nm for rising the film thickness from 4.88 μm to 7.23 μm, The difference between practical and simulated fringe visibility was monitored. So, the idea of the desired output can be achieved by simulating the mathematical model of an FPI-based optical fiber sensor before performing the lab experiments.

关键词： Scanning electron microscopy Optical fiber sensors Analytical models Simulation signal processing robot sensing systems Mathematical models Monitoring Fabry-Perot interferometers

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UAV-VLA: vision-Language-Action System for Large Scale Aerial Mission Generation 25

UAV-VLA: Vision-Language-Action System for Large Scale Aeria...

引用

ACM/IEEE international conference on Human-robot Interaction (HRI)

作者： Oleg Sautenkov Yasheerah Yaqoot Artem Lykov Muhammad Ahsan Mustafa Grik Tadevosyan Aibek Akhmetkazy Miguel Altamirano Cabrera Mikhail Martynov Sausar Karaf Dzmitry Tsetserukou Skoltech Moscow Russia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://***/sautenich/uav-vla

关键词： Visualization Navigation Human-robot interaction Nearest neighbor methods Autonomous aerial vehicles Natural language processing Satellite images Planning Trajectory robots

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Anomaly Segmentation of Leather Defects using Modified U-Net Architectures

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

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

SHAP-Integrated Convolutional Diagnostic Networks for Featur...

引用

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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C3D-VIT: Consistency-Aware 3D vision Transformer for Face Forgery Detection

C3D-VIT: Consistency-Aware 3D Vision Transformer for Face Fo...

引用

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

作者： Jingyi Zhang Peng Zhang Jingjing Wang Hikvision Research Institute

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, spatial and temporal inconsistencies have been shown to effectively enhance the generalization performance of face forgery detection, as common forgery strategies create inconsistencies among face regions and across frames. However, current methods often focus on either spatial or temporal modeling and involve complex modules that are difficult to integrate into other frameworks. Some approaches require extra training data or forgery masks to learn consistency features, limiting their applicability and performance. Additionally, most current spatio-temporal methods rely on CNNs, with few designed for transformers, making it challenging to adapt them for transformer use. To address these issues, we propose an efficient consistency modeling block that unifies spatial and temporal consistency modeling within a transformer framework. Specifically, we calculate the reconstruction error between features and predictions via neighboring spatial and temporal dimensions to explicitly model consistency without extra training data or forgery masks. This block can be seamlessly integrated into a transformer framework, adding less than 1% to computing costs.

关键词： Training Adaptation models Computer vision Three-dimensional displays Computational modeling Training data Transformers Forgery Speech processing Faces

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Event-based Sensor Fusion and Application on Odometry: A Survey

Event-based Sensor Fusion and Application on Odometry: A Sur...

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international Image processing, Applications and Systems conference (IPAS)

作者： Jiaqiang Zhang Xianjia Yu Ha Sier Haizhou Zhang Tomi Westerlund TIERS Lab University of Turku Turku Finland

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. They offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.

关键词： Surveys Visualization Laser radar Event detection robot vision systems Sensor fusion Sensor phenomena and characterization Cameras Odometry Next generation networking

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Image Super-Resolution Using Auto Encoders

Image Super-Resolution Using Auto Encoders

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international conference on Trends in Electronics and Informatics (ICEI)

作者： K. Sandhya Rani Pancheti Bhavya R. Vignesh E. Murali D. Deepa J. Cruz Antony B.E Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Image super-resolution (SR) remains a crucial problem in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoders, a specific kind of neural network whose task is to learn compressed representations of the high resolution images. In the context of the proposed model, there is an encoder, which, alternately, compresses the input and a decoder which reconstruct the output is proposed as capturing as well as reproducing all the details of an image. Experimental results show that the proposed method is one of the most efficient and accurate ones on benchmark datasets in terms of better details preservation and artefact reduction besides reducing processing time compared to the conventional methods. This makes this approach to be flexible and efficient to be used in medical imaging, satellite and digital photography making it an effective solution to improving image quality and resolution.

关键词： Image quality Computer vision Visualization Image coding Superresolution Neural networks signal processing algorithms signal processing Optical flow Image reconstruction

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