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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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MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Attention 31st

MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Att...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Yang, Enhui Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620630

The Segment Anything Model (SAM), introduced in 2023, has made significant advancements in the field of computer vision. However, SAM faces two major challenges: limitations related to single-scale processing and high computational demands for large-scale data handling, which restrict its widespread adoption in practical applications. Therefore, we propose MS-SAM to achieve better segmentation performance at a lower cost. We introduce a multi-scale image encoder to handle images of varying scales and complexities. Additionally, we propose a dynamic weighted agent attention, which can flexibly and efficiently adjust attention distribution while reducing computational costs. We train MS-SAM on a 1/20 subset of the SA-1B dataset for instance segmentation using points or boxes as prompts. The results show that we maintain the accuracy with the SAM method while reducing the parameters by about 6 times. We give sufficient experimental results to demonstrate its effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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RDM: A Light Field Super-Resolution Model Improved with Retinex Decomposition 16

RDM: A Light Field Super-Resolution Model Improved with Reti...

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16th international conference on Signal processing Systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, Xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： image acquisition

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8th international Symposium on Intelligent Informatics, ISI 2023

8th International Symposium on Intelligent Informatics, ISI ...

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8th international Symposium on Intelligent Informatics, ISI 2023

ISBN: (纸本)9789819721467

The proceedings contain 30 papers. The special focus in this conference is on Intelligent Informatics. The topics include: Multi-filter-Based image Pre-processing on Face Mask Detection Using Custom CNN Architecture;a Handy Simulated Radar Interface for Black Flight Identification System;Spatial Pyramid image Representation with DCT Features for Offline Signature Verification;Detection of AI Manipulated Videos Using Modern Deep Learning Algorithms;comprehensive Exploration of Deepfake Detection Using Deep Learning;Options Trading Strategy Based on GRU Forecasting;denoising Historical Text Documents Using Generative Adversarial Networks;comprehensive Survey of Audio-to-Text Conversion;Rainfall Forecasting Using High Spatiotemporal Satellite imagery and Machine Learning Techniques: A Case Study Using INSAT 3DR Data;the Personalization of Justified Recommendations Using the Users Profile Interest and Reviews;disease Detection in Tomato Plant Leaf Using Deep Learning Techniques;boosting Precision Agriculture Using Deep Learning Models on Edge Devices;comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism;DenseFed-PSO: Particle Swarm Optimization-Based DenseNet Federated Model in Alzheimer's Detection;a Machine Learning-Based Marine Vessel/Ship Classification Using Passive Sonar Signals—A Multi-class Problem;a computer-Aided Diagnosis System for the Detection of Parkinson’s Disease;Impact of the Use of Social Media on the Addiction and Social Isolation Levels of Adolescents After the COVID-19 Pandemic;preliminary Testing of a Color-Based Test Kit Detector for Bioplastics;applications of Artificial Intelligence in Biosensors;Enhancing Bamboo Dryer Using IOT Control;chaotic Resilience: Enhancing IoT Security Through Dynamic Data Encryption;Enhancement of Malware Detection Systems Using Mal-cGAN;similarity Learning and Genetic Algorithm Based Novel S-Box Optimization;A Survey on Deciphering of EEG Wa

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink 13

Research on Collaborative Filtering Algorithm of E-commerce ...

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13th international conference on Intelligent Control and Information processing, ICICIP 2025

作者： Zheng, Haohao Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In online shopping, a person's interest in a product is closely related to whether they will purchase it Analyzing people's interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user- item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the issue of matrix sparsity, but its accuracy in recommending products based on implicit feedback alone is insufficient This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by summing the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product's maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm's accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). © 2025 ieee.

关键词： Collaborative filtering

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Uncertainty-aware Correspondence Distillation for Deep image Clustering

Uncertainty-aware Correspondence Distillation for Deep Image...

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

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

ISBN: (纸本)9798350368741

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. © 2025 ieee.

关键词： image clustering Knowledge distillation Representation learning Semantic guidance

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 ieee.

关键词： denoising Medical image fusion text-guided fusion

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image Captioning with Multiple Perspectives—A Visual Context-Based Approach 5th

Image Captioning with Multiple Perspectives—A Visual Contex...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Ashwin, G. Chaitanya, V. Kishan Rohith Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

This study tackles the difficult issues of image captioning while negotiating the complexity of visual data processing. The complexity of visual data and the associated processing requirements make image captioning a daunting task. This research describes an effective method for image captioning that makes advantage of attentional mechanisms. Several models, including Transformer, VGG16, VGG19, Inception, RNN decoders, and Bahdanau’s attention mechanism, are contrasted and analyzed to demonstrate the benefits of integrating attention processes. By allowing the model to focus on the appropriate visual component, attention improves the accuracy of the generated captions. Transformer models, in particular, outperform other methods by capturing complicated relationships and delivering accurate output, and they have the highest BLEU Score of 70. The findings emphasize the significance of attention mechanisms and the relevance of selecting a suitable model architecture to maximize picture captioning performance. The variety of potential applications emphasizes the benefits and potential impact of image captioning in a broad range of scenarios. Several use cases are found beneficial, including supporting the visually impaired, improving product descriptions in e-commerce, assisting medical diagnosis, improving image search ability, and allowing effective communication and comprehension of visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Dual-Path Consistency Unsupervised Domain Adaptation for Nighttime Semantic Segmentation

Dual-Path Consistency Unsupervised Domain Adaptation for Nig...

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

作者： Lu, Yuwu Lang, Jicong Ding, Meirong School of Artificial Intelligence South China Normal University Foshan China

ISBN: (纸本)9798350368741

Nighttime semantic segmentation is an indispensable component in practical applications, such as automated vehicles. However, it is often hindered by the lack of annotations due to interference caused by inadequate lighting or exposure. To overcome these difficulties, we propose a Dual-Path Consistency (DPC) unsupervised domain adaptation (UDA) approach. One path is image Darkening Path (IDP), in which feature representations of original images and darkened images extracted from the feature encoder are leveraged to maintain cross-domain style consistency. Another path is image Masking Path (IMP), in which the masked images are reconstructed under the guidance of pseudo-labels, aiming to maintain content consistency in an entirely identical scenario. Extensive experiments on four benchmarks demonstrate the superior performance of the proposed DPC for nighttime semantic segmentation. © 2025 ieee.

关键词： image masking Nighttime semantic segmentation self-training unsupervised domain adaptation

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Unsupervised image-to-image Style Transfer via Dual-Condition Diffusion Models

Unsupervised Image-to-Image Style Transfer via Dual-Conditio...

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

作者： Fan, Anfei Yang, Jun Li, Wei Zhang, Chiyu College of Computer Science Sichuan Normal University Chengdu China

ISBN: (纸本)9798350368741

Style transfer is an artistic research topic within a series of generative tasks, and it is quite challenging to stably and reliably guide the generation of images with the expected content and style. Early methods followed a predefined style, defined by a reference image, and applied it to another image while preserving the latter's content structure, but the generated effects lacked high fidelity. As diffusion models have evolved, the content guidance for style transfer has transitioned from images to text, potentially leading to instance alterations. Generating with multiple conditions constrained in the latent space is challenging. Within the EDM framework, we have utilized cross-attention to design style and content embedding modules, overcoming instance alterations without the need for paired datasets and achieving high-resolution, high-fidelity results. Our experimental findings demonstrate that our architecture has advanced to the forefront of image-to-image style transfer capabilities. © 2025 ieee.

关键词： diffusion models image generation image-to-image style transfer

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Multiple Weather images Restoration Using the Task Transformer and Adaptive Mixup Strategy 41st

Multiple Weather Images Restoration Using the Task Transfor...

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41st computer Graphics international conference, CGI 2024

作者： Wen, Yang Lai, Anyu Qian, Bo Wang, Hao Shi, Wuzhen Cao, Wenming Guangdong Provincial Key Laboratory of Intelligent Information Processing School of Electronic and Information Engineering Shenzhen University Shenzhen China The Department of Comupter Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031818059

The current state-of-the-art in severe weather removal predominantly focuses on single-task applications, such as rain removal, haze removal, and snow removal. However, real-world weather conditions often consist of a mixture of several weather types, and the degree of weather mixing in autonomous driving scenarios remains unknown. In the presence of complex and diverse weather conditions, a single weather removal model often encounters challenges in producing clear images from severe weather images. Therefore, there is a need for the development of multi-task severe weather removal models that can effectively handle mixed weather conditions and improve image quality in autonomous driving scenarios. In this paper, we introduce a novel multi-task severe weather removal model that can effectively handle complex weather conditions in an adaptive manner. Our model incorporates a weather task sequence generator, enabling the self-attention mechanism to selectively focus on features specific to different weather types. To tackle the challenge of repairing large areas of weather degradation, we introduce Fast Fourier Convolution (FFC) to increase the receptive field. Additionally, we propose an adaptive upsampling technique that effectively processes both the weather task information and underlying image features by selectively retaining relevant information. Our proposed model has achieved state-of-the-art performance on the publicly available dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Restoration

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