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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 ieee.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide image Classification

Pathological Prior-Guided Multiple Instance Learning For Mit...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zheng, Weixi Huang, Aoling Yuan, Jingping Zhao, Haoyu Zhao, Zhou Xu, Yongchao Géraud, Thierry School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China School of Computer Science Central China Normal University Wuhan China EPITA Research Laboratory Le Kremlin-Bicêtre France

ISBN: (纸本)9798350368741

In histopathology, intelligent diagnosis of Whole Slide images (WSIs) is essential for automating and objectifying diagnoses, reducing the workload of pathologists. However, diagnostic models often face the challenge of forgetting previously learned data during incremental training on datasets from different sources. To address this issue, we propose a new framework PaGMIL to mitigate catastrophic forgetting in breast cancer WSI classification. Our framework introduces two key components into the common MIL model architecture. First, it leverages microscopic pathological prior to select more accurate and diverse representative patches for MIL. Secondly, it trains separate classification heads for each task and uses macroscopic pathological prior knowledge, treating the thumbnail as a prompt guide (PG) to select the appropriate classification head. We evaluate the continual learning performance of PaGMIL across several public breast cancer datasets. PaGMIL achieves a better balance between the performance of the current task and the retention of previous tasks, outperforming other continual learning methods. Our code will be open-sourced upon acceptance. © 2025 ieee.

关键词： Breast Cancer Continual Learning Multiple Instance Learning Whole Slide image Classification

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using Machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing Vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing computer Vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered Machine Vision applications.

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843969

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DiffuseFIST: A Fast image-guided Style Transfer Method for Adapting Large-scale Diffusion Models

DiffuseFIST: A Fast Image-guided Style Transfer Method for A...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Dai, Miaomiao Zhou, Qianyu Yi, Ran Ma, Lizhuang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Pre-trained text-to-image (T2I) synthesis diffusion models (DM) have shown remarkable capabilities in generating diverse images. However, they struggle to satisfy the user's requirements due to (i) text's inherent imprecision in expressing specific styles and (ii) generation is time-consuming due to many iterations in reverse process of diffusion models. To address these issues, we propose a fast style transfer method adopting pre-trained large-scale diffusion models, dubbed as DiffuseFIST, which adds T-small (300) noise to accelerate reverse process and solely requires real-world images and artistic images as input. Specifically, to preserve content and prevent style leakage, we introduce Content Injection (CI) strategy to achieve fine-grained control over the generated structure by manipulating spatial features and self-attention inside the model. Furthermore, we design Iterative Style Guidance (ISG) strategy which allows explicit user guidance and control of stylization tradeoffs. Finally, we initialize latent variable with Whitening and Coloring Transform (WCT) to deal with the disharmonious color. Qualitative and quantitative experiments demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer methods. © 2025 ieee.

关键词： content generation diffusion models style transfer

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Landsat8-9 Satellite image Classification with ERDAS for Mapping the Urban Spaces of East Sikkim

Landsat8-9 Satellite Image Classification with ERDAS for Map...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Chunnu Khawas Mohan P. Pradhan Ratika Pradhan Ashis Pradhan Dept of Computer Applications Sikkim University Sikkim India Dept. of CSE Sikkim Manipal Ins. of Tech. Sikkim Manipal University Sikkim India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research initiative examines the categorization of Landsat 8-9 satellite imagery from the Operational Land imager Sensor(OLI) using remote sensing to quantify land cover and delineate urban areas in East Sikkim for the year 2023. It employs a system for satellite image processing utilizing supervised classification methods. The processing was transitioned to spectrum preparation and enhancement of spatial resolution utilizing the blue, green, red, near-infrared, and panchromatic channels. LandSat 8-9 OLI and the Thermal Infrared Sensor (TIRS) satellites image has been used for this study. ERDAS Imagine software has been used for performing image classification. The validation of the findings from the categorization has been performed using multiple reference points, including a previously validated national processing result and a Google Earth image from the same year. Upon multiple iterations of image classification, the supervised method achieved overall accuracy of 91% and Kappa indices of 0.84 for the Maximum Likelihood classification technique where as the unsupervised method achieved overall accuracy of 76% and Kappa indices of 0.63 for ISODATA classification technique.

关键词： Earth Artificial satellites Accuracy Urban areas Landsat Thermal sensors Satellite images Spatial resolution Remote sensing image classification

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 ieee.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Analytical Insight into Cutting-Edge image Captioning for Advanced ChatGPT Functionality 2nd

Analytical Insight into Cutting-Edge Image Captioning for Ad...

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2nd international conference on Business Data Analytics, ICBDA 2023

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India SCSET Bennett University Greater Noida India

ISBN: (纸本)9783031807770

At present, Artificial intelligence (AI) and Machine learning (ML) algorithm contribute to transforming the technology environment. Specifically, the development of chatbots has been on a rapid-fire overhead in recent years. The image captioning with ChatGPT is most growing field for text generating for the future. In recent years, the innovation of virtual assistants has boomed with ChatGPT becoming an increasingly popular language model of artificial intelligence. This work evaluates the principles, goals, and problems of ChatGPT in the present study. This study explores the technology's background, growth, and development, along with its applications. In addition, we will look at the benefits of connecting information by ChatGPT in image processing and content writing that the image exposes. However, we believe that ChatGPT will contribute to improving human-AI interaction and bridging scientific gaps by focusing on various research and development opportunities, such as energy conservation, privacy, and security, expanding its scope to other technologies like robotics and computer vision, deep learning, and ML. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ChatGPT model computer vision NLP

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SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2

SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned ...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Xinrun Wang, Chengliang Ning, Haojian Zhang, Mengzhan Shen, Mei Li, Shiying College of Computer Science Chongqing University Chongqing China Department of Ophthalmology Xiang'an Hospital of Xiamen University Xiamen China

ISBN: (纸本)9798350368741

Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model (SAM) version 2, enabling the tracking and segmentation of specified objects across the OCTA scanning layer sequence. To further this work, a prompt point generation strategy in frame sequence and a sparse annotation method to acquire retinal vessel (RV) layer masks are proposed. This method is named SAM-OCTA2 and has been experimented on the OCTA-500 dataset. It achieves state-of-the-art performance in segmenting the foveal avascular zone (FAZ) on regular 2D en-face and effectively tracks local vessels across scanning layer sequences. The code is available at: https://***/ShellRedia/SAM-OCTA2. © 2025 ieee.

关键词： fine-tuning image segmentation OCTA segment anything model sparse annotation

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An image processing-Based Tomato Leaf Disease Prediction Using Deep CNN 5th

An Image Processing-Based Tomato Leaf Disease Prediction Usi...

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5th international conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Vanisri, Kesana Raju, K. Srujan Laxmaiah, Bagam Department of CSE CMR Technical Campus Hyderabad India

ISBN: (纸本)9783031751660

Pest outbreaks have a significant impact on plants and crops, important to a reduction in national agricultural productivity. Conventionally, farmers and specialists rely on physical observation, and it can be timeconsuming, more expensive, and prone to errors, to classify diseases in plants. Though, by employing leading-edge image processing techniques, an instinctive detection system can be established, providing rapid and precise results. This paper aims to produce a Tomato Leaf Disease identification model with different categorizing leaf images and utilizing deep neural networks. The development of specific plant protection has the potential for substantial growth and improvement, with computer vision applications in the precision agriculture market also showing promising growth. This discussion about outlines all the critical stages for implementing the disease recognition model, initial from the image database group, assessment by agricultural specialists, and applying a deep learning framework to conduct the training of deep CNNs. By using this CNN, we will apply Max pooling layer, Multiple implementations are applied and finally we will get more accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dataset Deep CNN Diseases neural network

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