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检索条件"任意字段=2022 International Conference on Image Processing and Media Computing, ICIPMC 2022"

共 2365 条记录，以下是1-10 订阅

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Proceedings - 2022 international conference on image processing and media computing, icipmc 2022

Proceedings - 2022 International Conference on Image Process...

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2022 international conference on image processing and media computing, icipmc 2022

ISBN: (纸本)9781665468725

The proceedings contain 29 papers. The topics discussed include: an adaptive detection method for early smoke of coal mine fire based on local features;application of deep convolution neural network algorithm in detecting traditional calligraphy characters;joint optimization of multiple loss function for fine-grained image recognition;regional proposal based underwater object tracking;automatic discrimination between normal and diabetic retinopathy fundus images using machine learning classification algorithm;feature refinement network for crowd counting;a double-stream exchange transformer network for intrinsic image decomposition;research on fast fusion method of digital camouflage and background;and image analysis of facial blood vessels for anti-spoofing of printed image and 3D mask attacks.

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Proceedings - 2023 2nd international conference on image processing and media computing, icipmc 2023

Proceedings - 2023 2nd International Conference on Image Pro...

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2nd international conference on image processing and media computing, icipmc 2023

ISBN: (纸本)9798350326611

The proceedings contain 30 papers. The topics discussed include: dynamic digital signage system: a cost-effective and unified web-based solution for content and analytics management;bronze corrosion identification method based on hyperspectral image unmixing;manifold proximal gradient with momentum for sparse PCA;research on the application of digital media art in museum exhibition - take Nanjing City wall museum as an example;dual adversarial contrastive learning for underwater image enhancement;low-illumination image enhancement based on end-to-end network using attention module;channel and spatial transformer for underwater image enhancement;ACE-HF framework: enhancing implicit information in hyperspectral lacquer coffin paintings through automatic color enhancement and homomorphic filtering;and augmented reality system based on real-time object 6D pose estimation.

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2024 3rd international conference on image processing and media computing, icipmc 2024

2024 3rd International Conference on Image Processing and Me...

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3rd international conference on image processing and media computing, icipmc 2024

ISBN: (纸本)9798350386660

The proceedings contain 58 papers. The topics discussed include: real-time heart rate detection based on body surface video data;a local dimming algorithm based on deep learning;multilevel interaction embedding for hyperspectral image super-resolution;a review of point target and extended target tracking algorithms;phase retrieval algorithm based on transport of intensity equation under the fusion of regularization and grating modulation;exploring data augmentation effects on a singular illumination distribution dataset with ColorJitter;unsupervised domain adaptation for cross-modality cardiac image segmentation based on contrastive image synthesis;a novel color image encryption scheme based on fractional-order chaotic system;and contextual transformer based small targets detection for cervical cell.

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Evaluating the Fidelity of image Captioning via Weighted Boolean Question Answering 13th

Evaluating the Fidelity of Image Captioning via Weighted Boo...

引用

13th international conference on Natural Language processing and Chinese computing

作者： Wang, Kaixuan Li, Shasha Tang, Jintao Long, Kehan Miao, Yongzhu Chen, Fangda Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

image captioning evaluation is of great significance in guiding caption generation, practically valuable evaluation requires fine-grained evaluation metrics. However, most of the current metrics are only capable of measuring the overall quality of the caption, which means users are not informed of any details about the caption's defects when given a low score. As fidelity is one of the quality criteria of image captioning and has a great impact on the application, we proposed WBQA, a metric that focuses on evaluating the fidelity of the caption by Weighted Boolean Question Answering. WBQA can display which part of captions is unfaithful because natural language is used to express questions to check whether the caption matches the image. Experiments show that our metric is excellent for measuring fidelity (from 0.31 and 0.34 to 0.52 and 0.58 with Pearson correlation coefficient) and achieves state-of-the-art on multiple image captioning evaluation datasets.

关键词： image captioning evaluation Fidelity Question answering

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A Survey of Zero-Shot Stance Detection 13th

A Survey of Zero-Shot Stance Detection

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13th international conference on Natural Language processing and Chinese computing

作者： Liu, Guangzhen Zhao, Kai Zhang, Linlin Bi, Xuehua Lv, Xiaoyi Chen, Cheng Xinjiang Univ Sch Comp Sci & Technol Urumqi 830049 Xinjiang Peoples R China Xinjiang Univ Sch Software Xi Urumqi 830049 Xinjiang Peoples R China Xinjiang Med Univ Coll Med Engn & Technol Urumqi 830017 Xinjiang Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Stance detection is a subproblem of sentiment analysis, commonly defined as classifying the stance of a text toward a certain target: {Favor, Against, Neither}. As an important research problem, reliance on high-quality annotated data poses a significant challenge. However, in the real world, with the rapid development of social media, it is impossible to annotate the massive amount of text on diverse topics, a universal framework for stance detection is expected. Consequently, zero-shot stance detection methods that do not require annotated data have attracted researchers' attention. In this survey, we review and introduce the early research and definition of stance detection, with a focus on summarizing the current research status of ZSSD, discuss datasets and the most advanced models. Finally, based on the above research, we explore possible future directions.

关键词： Natural language processing Deep learning Stance detection Zero-shot

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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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Fuzzy-AttnNet: A Deep Learning Framework for image Retrieval Using Fuzzy Feature Fusion 16

Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval...

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2024 16th international conference on Graphics and image processing, ICGIP 2024

作者： Liu, Shengyu Pan, Jingshan Li, Na Hu, Tao Tian, Tangbin Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. The model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. The fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. The experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval. © 2025 SPIE.

关键词： Medical imaging

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Seg-diffusion: Text-to-image Diffusion Model for Open-Vocabulary Semantic Segmentation

Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabu...

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

作者： Zhang, Shuo Huang, Jiaming Wu, Yan Hu, Tao Tang, Wenbing Liu, Jing Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Open-vocabulary semantic segmentation (OVSS) is a challenging computer vision task that labels each pixel within an image based on text descriptions. Recent advancements in OVSS are largely attributed to the increased model capacity. However, these models often struggle with unfamiliar images or unseen text, as their visual language understanding is limited to training data. Text-to-image (T2I) diffusion models have demonstrated strong image generation with diverse open-vocabulary descriptions. It prompted us to explore whether the comprehensive priors in T2I diffusion models could enhance the zero-shot generalization of OVSS. In this study, we define OVSS as a denoising diffusion task from noisy to object mask and introduce Seg-diffusion, a novel method based on Stable Diffusion that utilizes its extensive visual and linguistic prior knowledge. Specifically, the object mask diffuses from ground-truth to a random distribution in latent space. The model learns to reverse this noisy process to reconstruct object mask to segment objectives using text embeddings with our proposed Content Attention Module (CAM). Extensive experiments on popular OVSS benchmarks show that Seg-diffusion outperforms previous well-established methods and achieves impressive zero-shot generalization to unseen datasets. © 2025 IEEE.

关键词： content attention module fine-tuning protocol open-vocabulary semantic segmentation text-to-image LDM

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CurMIM: Curriculum Masked image Modeling

CurMIM: Curriculum Masked Image Modeling

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

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 IEEE.

关键词： Curriculum Learning image Representation Masked image Modeling Visual Pre-training

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Computer-Generated image Forensics Based on Vision Transformer with Forensic Feature Pre-processing Module 6th

Computer-Generated Image Forensics Based on Vision Transfor...

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6th international conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Chen, Yifang Wen, Guanchen Wang, Yong Yang, Jianhua Zhang, Yu Guangdong Polytechnic Normal University GuangDong GuangZhou510665 China

ISBN: (纸本)9783031736988

The correct distinction between highly realistic computer-generated (CG) images and photographic (PG) images has become an important area of research. In recent years, most of the CG image forensics methods are proposed based on deep learning, but the detection performances of these methods still need to be improved, especially in terms of robustness and generalization. To tackle these issues, we leverage the Vision Transformer (ViT) model, which excels in capturing the global features of images, and design a Forensic Feature Pre-processing (FFP) module to further improve the detection performance. Experiments are conducted on a large-scale CG image benchmark (LSCGB), which is a challenging dataset for CG image detection. The proposed approach can achieve high detection accuracy. Extensive experiments on different public datasets and common post-processing operations demonstrate our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Computer forensics

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