检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

3,098 篇 会议
54 册 图书
12 篇 期刊文献

馆藏范围

3,164 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,080 篇 工学
- 1,683 篇 计算机科学与技术...
- 1,003 篇 软件工程
- 845 篇 信息与通信工程
- 487 篇 电气工程
- 320 篇 光学工程
- 267 篇 生物工程
- 164 篇 生物医学工程（可授...
- 150 篇 控制科学与工程
- 112 篇 电子科学与技术（可...
- 86 篇 化学工程与技术
- 79 篇 机械工程
- 55 篇 材料科学与工程（可...
- 50 篇 动力工程及工程热...
- 50 篇 安全科学与工程
- 48 篇 仪器科学与技术
- 45 篇 交通运输工程
- 44 篇 网络空间安全
- 31 篇 土木工程
852 篇 理学
- 459 篇 物理学
- 280 篇 生物学
- 255 篇 数学
- 89 篇 化学
- 84 篇 统计学（可授理学、...
584 篇 医学
- 577 篇 临床医学
- 74 篇 基础医学(可授医学...
- 63 篇 药学(可授医学、理...
274 篇 管理学
- 161 篇 图书情报与档案管...
- 137 篇 管理科学与工程(可...
- 36 篇 工商管理
35 篇 法学
- 32 篇 社会学
24 篇 农学
16 篇 教育学
12 篇 经济学
11 篇 文学
3 篇 军事学

主题

389 篇 visual communica...
280 篇 visualization
239 篇 image coding
232 篇 image processing
197 篇 deep learning
163 篇 training
158 篇 feature extracti...
114 篇 image enhancemen...
108 篇 image segmentati...
105 篇 semantics
98 篇 three-dimensiona...
94 篇 object detection
88 篇 convolution
87 篇 computer vision
82 篇 image reconstruc...
79 篇 image recognitio...
72 篇 video coding
70 篇 neural networks
70 篇 image color anal...
68 篇 image classifica...

机构

35 篇 shanghai jiao to...
24 篇 shanghai jiao to...
22 篇 school of electr...
21 篇 institute of ima...
20 篇 university of sc...
19 篇 beijing universi...
18 篇 tianjin univ sch...
15 篇 university of el...
11 篇 school of remote...
10 篇 shanghai jiao to...
9 篇 zhejiang univers...
9 篇 univ elect sci &...
9 篇 university of ch...
9 篇 school of electr...
8 篇 cas key laborato...
8 篇 tencent media la...
7 篇 univ chinese aca...
7 篇 shenzhen interna...
7 篇 univ sci & techn...
7 篇 school of inform...

作者

31 篇 zhai guangtao
24 篇 li sumei
24 篇 sumei li
21 篇 song li
19 篇 min xiongkuo
19 篇 shan liu
17 篇 guangtao zhai
17 篇 li li
16 篇 paik joonki
15 篇 zhenzhong chen
13 篇 xie rong
13 篇 dong liu
12 篇 li song
12 篇 xiongkuo min
12 篇 xin jin
11 篇 jin xin
11 篇 yang hua
11 篇 chen li
10 篇 heming sun
10 篇 zhibo chen

语言

3,153 篇 英文
10 篇 其他
3 篇 中文
1 篇 西班牙文

检索条件"任意字段=2021 International Conference on Visual Communications and Image Processing, VCIP 2021"

共 3164 条记录，以下是11-20 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Foreground Prediction for image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

引用

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

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

VisTa: visual-contextual and Text-augmented Zero-shot Object-level OOD Detection

VisTa: Visual-contextual and Text-augmented Zero-shot Object...

引用

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

作者： Zhang, Bin Qu, Xiaoyang Li, Guokuan Wan, Jiguang Wang, Jianzong Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original training data, the challenge of zero-shot object-level out-of-distribution (OOD) detection arises. This task becomes crucial in ensuring the reliability of detectors in open-world settings. While existing methods have demonstrated success in image-level OOD detection using pre-trained vision-language models like CLIP, directly applying such models to object-level OOD detection presents challenges due to the loss of contextual information and reliance on image-level alignment. To tackle these challenges, we introduce a new method that leverages visual prompts and text-augmented in-distribution (ID) space construction to adapt CLIP for zero-shot object-level OOD detection. Our method preserves critical contextual information and improves the ability to differentiate between ID and OOD objects, achieving competitive performance across different benchmarks. © 2025 IEEE.

关键词： vision-language representations visual prompt Zero-shot object-level OOD detection

来源：评论

学校读者我要写书评

暂无评论

Bibliometric Analysis of Road Distress Detection Based on Citespace 24

Bibliometric Analysis of Road Distress Detection Based on Ci...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Li, Yaoye Zhang, Yinbo He, Rong Ma, Lianxia Chen, Fang Xinjiang communications Construction Group Co. Ltd. Xinjiang Urumqi China Xinjiang Key Laboratory of Transportation Prefabricated Construction and Maintenance Xinjiang Urumqi China Xinjiang University Xinjiang Urumqi China

ISBN: (纸本)9798400709999

To analyze the research progress in the field of road disease detection, this article searched the Web of Science Core Collection database for papers related to road disease detection from 2000 to *** CiteSpace software, a visual analysis was conducted on the 247 retrieved documents, focusing on annual publication numbers, publishing countries, core institutions and authors, keywords, burst words, and timeline mapping to explore the current status and trends in this *** findings indicate that since 2004, the overall research trend in road disease detection has been on an upward trajectory, with a notable increase in the number of research papers published in 2021, reflecting a significant surge in research ***, China, the United States, and Italy have been the most prolific in research *** leading research institutions are primarily scientific and technological academies, with Chang'an University taking a dominant position in the field of road disease *** keyword co-occurrence analysis, burst detection, and keyword clustering analysis, it was determined that disease detection technology, deep learning and machine learning, image processing and intelligent analysis, and system integration and development have been the research hotspots in the road disease detection field from 2000 to *** is anticipated that future research will evolve towards the directions of performance evaluation, multimodal data fusion, and real-time monitoring and analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data fusion

来源：评论

学校读者我要写书评

暂无评论

ZVEFusion: Zero-Shot visual Enhancement Fusion for Infrared and Visible images in Low Light

ZVEFusion: Zero-Shot Visual Enhancement Fusion for Infrared ...

引用

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

作者： Liu, Duo Shi, Yiqi Zhang, Guoyin Li, Sizhao Zhang, Liguo School of Computer Science and Technology Harbin Engineering University China

ISBN: (纸本)9798350368741

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception. © 2025 IEEE.

关键词： image Fusion Low Light visually Enhanced Zero-Shot

来源：评论

学校读者我要写书评

暂无评论

LaTeXNet: A Specialized Model for Converting visual Tables and Equations to LaTeX Code

LaTeXNet: A Specialized Model for Converting Visual Tables a...

引用

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

作者： Xia, Renqiu Zhou, Hongbin Feng, Ziming Liu, Huanxi Chen, Boan Zhang, Bo Yan, Junchi Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China China Merchants Bank Credit Card Center China

ISBN: (纸本)9798350368741

LaTeX provides precise representation of complex elements (i.e., tables and equations) in scientific documents. However, the automated transcription of visual representations into LaTeX code is challenging and prone to errors. This paper introduces LaTeXNet, a specialized model designed to automate the conversion of visual tables and equations into LaTeX code. First, we develop an automated annotation tool that extracts image-LaTeX pairs for tables, equations, and text paragraphs with inline equations from arXiv platform, creating the MM-LaTeX dataset with over 2.5M pairs. Moreover, we design the LaTeXNet model, trained on MM-LaTeX, which unifies the conversion of Tables, Equations, and TextEqs. Our experimental results indicate that LaTeXNet surpasses both open-source and commercial, closed-source models in Table-to-LaTeX, Equation-to-LaTeX and TextEq-to-LaTeX tasks. © 2025 IEEE.

关键词： Document Understanding Equation-to-LaTeX Table-to-LaTeX TextEq-to-LaTeX Vision Language

来源：评论

学校读者我要写书评

暂无评论

Trilinear Distillation Learning and Question Feature Capturing for Medical visual Question Answering 5th

Trilinear Distillation Learning and Question Feature Capturi...

引用

5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Long, Shaopei Li, Yong Weng, Heng Tang, Buzhou Wang, Fu Lee Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China Harbin Inst Technol Dept Comp Sci Shenzhen Grad Sch Shenzhen Guangdong Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Medical visual Question Answering (Med-VQA) targets at answering a clinical question associated with a corresponding medical image. Med-VQA has revealed huge potential in the field of medicine, but it still faces many challenges in practice. Existing Med-VQA models have not fully utilized medical answer features, though Compact Trilinear Interaction (CTI) model has proven that the answer has close correlations with question and image. However, directly applying existing CTI model in general domain on the small volume of Med-VQA datasets would lead to over-fitting and obtain poor performance. Therefore, this paper proposes a novel trilinear distillation learning framework called TDL for MedVQA to learn correlations between medical answer, image and question from the trilinear model by distillation learning. In addition, considering the clinical questions are harder to understand due to the professionalism of medical scenarios, we design a question feature capturing (QFC) module to capture the fine-grained intramodality relationships and characteristics of clinical questions. Furthermore, we take account of the unbalanced-labels of Med-VQA datasets, and propose a novel Label Smoothing Regularization Focal Loss to enhance the generalization capability of the model while dynamically adjust the sample weights during training. Based on the standard benchmark dataset VQA-RAD, our proposed TDL model achieves the state-of-the-art performance, including the best overall Accuracy 76.71%, Precision 83.78%, Recall 76.71% and F1-score 78.94%.

关键词： Medical visual Question Answering Trilinear Distillation Learning Question Feature Capturing

来源：评论

学校读者我要写书评

暂无评论

CurMIM: Curriculum Masked image Modeling

CurMIM: Curriculum Masked Image Modeling

引用

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

来源：评论

学校读者我要写书评

暂无评论

Progressive Enhancement of Anatomical Structural and Medical Feature Learning for Cephalometric Landmark Detection 24

Progressive Enhancement of Anatomical Structural and Medical...

引用

7th international conference on Digital Medicine and image processing, DMIP 2024

作者： Li, Mengqi Qi, Senrong Zhu, Xuemei Niu, Kai Han, Jianhui Li, Ying He, Zhiqiang Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing Stomatological Hospital & School of Stomatology Capital Medical University Beijing China Beijing University of Posts and Telecommunications Hospital Beijing China

ISBN: (纸本)9798400709586

Landmark detection is an essential foundation of cephalometric analysis, playing a crucial role in clinical diagnosis and treatment of orthodontic and orthognathic conditions. This paper proposes a novel framework that enables targeted learning and extraction of structural and medical features based on neural network. Specifically, our framework's key components comprise Contour Sharpener, Coupling Transformer, and Progressively Targeted Feature Extraction Networks (PT-FENet). The Contour Sharpener intensifies contour features informed by the prior distributions of landmarks, enabling the network to learn anatomical structural features. The Coupling Transformer couples and transforms associated landmarks based on the similarity of features around medically related landmarks and the invariant structure of geometrically constrained landmarks to enhance the learning of anatomical structures and medical correlations among them. PT-FENet fully extracts multi-scale features and progressively targets the refinement of anatomical structural and medical features grounded in prior knowledge, thereby augmenting the attention towards detection information. Experimental results demonstrate that our method outperforms existing state-of-the-art methods on public dataset. Our method enables rapid batch processing of images for precise landmark detection, thus possessing significant value in clinical cephalometric analysis and orthodontic applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Batch data processing

来源：评论

学校读者我要写书评

暂无评论

Imagined Speech and visual imagery as Intuitive Paradigms for Brain-Computer Interfaces 13

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Lee, Seo-Hyun Park, Ji-Ha Kim, Deok-Seon Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Dept. of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance. © 2025 IEEE.

关键词： Vision

来源：评论

学校读者我要写书评

暂无评论

Towards Maximizing Semantic Coverage for image-Text Retrieval

Towards Maximizing Semantic Coverage for Image-Text Retrieva...

引用

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

作者： Xu, Junhao Liu, Zheng Chen, Zhumin Shen, Fei School of Computer Science and Technology Shandong University China School of Computer Science and Technology Shandong University of Finance and Economics China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798350368741

To establish semantic associations between images and texts, existing image-Text Retrieval (ITR) methods primarily focus on fixed-scale fragments, which only identify explicit semantic categories. Consequently, semantic coverage is constrained, leading to the omission of certain semantic associations. To enlarge the semantic coverage, we propose the Semantic Coverage-Aware Network (SCA-Net). First, explicit semantic categories are identified by SCA-Net through analyzing the semantic membership of visual and textual fragments, thereby establishing more precise explicit semantic associations. Second, implicit semantic categories are identified by SCA-Net via adaptively aggregating visual and textual fragments across various scales using a co-occurrence-aware router, thereby significantly expanding the semantic coverage and establishing complete semantic associations. Third, image-text similarity is calculated using the attention mechanism over a broader range of semantic coverage. Extensive experiments demonstrate that SCA-Net significantly enhances ITR performance compared to state-of-the-art methods by maximizing semantic coverage. © 2025 IEEE.

关键词： Co-occurrence-aware router image-text retrieval Semantic association Semantic category Semantic coverage

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共317页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：