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

22,998 篇 会议
107 册 图书
93 篇 期刊文献

馆藏范围

23,197 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,621 篇 工学
- 11,107 篇 计算机科学与技术...
- 3,478 篇 软件工程
- 2,445 篇 机械工程
- 1,715 篇 光学工程
- 1,076 篇 电气工程
- 1,013 篇 控制科学与工程
- 784 篇 信息与通信工程
- 411 篇 仪器科学与技术
- 352 篇 生物工程
- 251 篇 生物医学工程（可授...
- 196 篇 电子科学与技术（可...
- 114 篇 化学工程与技术
- 107 篇 安全科学与工程
- 100 篇 测绘科学与技术
- 88 篇 建筑学
- 85 篇 交通运输工程
- 84 篇 土木工程
3,494 篇 医学
- 3,481 篇 临床医学
- 81 篇 基础医学(可授医学...
3,240 篇 理学
- 1,939 篇 物理学
- 1,639 篇 数学
- 563 篇 统计学（可授理学、...
- 500 篇 生物学
- 249 篇 系统科学
- 106 篇 化学
521 篇 管理学
- 311 篇 图书情报与档案管...
- 223 篇 管理科学与工程(可...
- 76 篇 工商管理
276 篇 艺术学
- 276 篇 设计学（可授艺术学...
66 篇 法学
- 63 篇 社会学
38 篇 农学
28 篇 教育学
22 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,186 篇 computer vision
3,967 篇 pattern recognit...
3,005 篇 training
2,007 篇 computational mo...
1,817 篇 visualization
1,815 篇 cameras
1,515 篇 feature extracti...
1,481 篇 shape
1,455 篇 three-dimensiona...
1,438 篇 image segmentati...
1,287 篇 robustness
1,205 篇 computer archite...
1,155 篇 semantics
1,147 篇 conferences
1,107 篇 layout
1,093 篇 computer science
1,088 篇 object detection
1,025 篇 benchmark testin...
970 篇 codes
922 篇 face recognition

机构

136 篇 univ sci & techn...
121 篇 univ chinese aca...
118 篇 chinese univ hon...
107 篇 carnegie mellon ...
101 篇 tsinghua univers...
101 篇 microsoft resear...
95 篇 swiss fed inst t...
93 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
80 篇 university of ch...
77 篇 shanghai ai lab ...
72 篇 shanghai jiao to...
69 篇 national laborat...
67 篇 microsoft res as...
67 篇 alibaba grp peop...
64 篇 adobe research
62 篇 tsinghua univ pe...
60 篇 peking univ peop...
59 篇 univ oxford oxfo...

作者

81 篇 van gool luc
72 篇 timofte radu
64 篇 zhang lei
47 篇 luc van gool
40 篇 yang yi
40 篇 li stan z.
37 篇 loy chen change
34 篇 chen chen
33 篇 xiaoou tang
32 篇 liu yang
32 篇 qi tian
31 篇 tian qi
31 篇 sun jian
30 篇 murino vittorio
30 篇 pascal fua
29 篇 darrell trevor
29 篇 li fei-fei
28 篇 li xin
28 篇 ying shan
27 篇 vasconcelos nuno

语言

23,131 篇 英文
38 篇 其他
22 篇 中文
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops"

共 23198 条记录，以下是61-70 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：

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Segment Anything in Medical Images with nnUNet

Segment Anything in Medical Images with nnUNet

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

MADation: Face Morphing Attack Detection with Foundation Models

MADation: Face Morphing Attack Detection with Foundation Mod...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Eduarda Caldeira Guray Ozgur Tahar Chettaoui Marija Ivanovska Peter Peer Fadi Boutros Vitomir Struc Naser Damer Fraunhofer IGD Germany University of Ljubljana Slovenia TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD, FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pretraining. In this work, we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end, we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework, MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD, we publicly release the implementation of MADation at https://***/gurayozgur/MADation.

关键词： Training computer vision Frequency modulation Foundation models Face recognition conferences Training data Benchmark testing Transformers Reproducibility of results

来源：评论

学校读者我要写书评

暂无评论

Evaluating Multimodal vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding

Evaluating Multimodal Vision-Language Model Prompting Strate...

引用

ieee Winter Applications and computer vision workshops (WACVW)

作者： Aryan Keskar Srinivasa Perisetla Ross Greer University of California Merced

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Understanding complex traffic scenes is a crucial challenge in advancing autonomous driving systems. Visual Question Answering (VQA) tasks have emerged as a promising approach to extracting actionable insights from multimodal traffic data, enabling vehicles to make accurate, real-time decisions. The MAPLM-QA dataset, introduced as part of the 2025 WACV Large Language vision Models Challenge for Autonomous Driving (LLVM-AD), offers a robust benchmark for this task, comprising 14,000 multi-modal frames combining high-resolution panoramic images and rendered Bird's Eye View (BEV) depictions of LiDAR 3D point clouds. In this work, we explore the application of NVIDIA's vision-Language Model (ViLA) to address VQAs in MAPLM-QA. By employing detailed prompt engineering tailored to the dataset, we systematically evaluate ViLA's performance, identifying strengths in certain metrics such as quality assessment while highlighting challenges in lane counting, intersection recognition, and nuanced scene understanding. Our findings illustrate the potential of vision-Language Models (VLMs) in enhancing traffic scene analysis and autonomous driving, establishing a strong foundation and analysis of limitations for future research in leveraging VLMs and multimodal datasets toward scalable, robust traffic scene understanding.

关键词： Point cloud compression Visualization Solid modeling Three-dimensional displays Roads Question answering (information retrieval) Real-time systems Quality assessment Prompt engineering Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Interaction with Virtual Agents: A Pick-up and Placement Case Study

Multi-modal Interaction with Virtual Agents: A Pick-up and P...

引用

Virtual Reality and 3D User Interfaces Abstracts and workshops (VRW), ieee conference on

作者： Jalal Safari Bazargani Soo-Mi Choi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a multi-modal human-agent interaction framework that integrates hand tracking, voice commands, user intention interpretation, and scene understanding using large language and vision models for object pick-up and placement tasks in VR. Scene understanding integrates object tags and ChatGPT-4V’s object detection. User commands are processed via the LLaMA-3.2 model, which interprets user intention, identifies task and reference objects, and spatial relationships for task execution. An initial experiment with 15 participants showed that the multimodal approach achieved the fastest task completion times and highest user satisfaction. While hand-only interaction was comparable, voice-only interaction was significantly slower.

关键词： Hands Solid modeling Three-dimensional displays conferences Virtual reality Object detection User interfaces Object recognition

来源：评论

学校读者我要写书评

暂无评论

AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

引用

2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Karthikeyan, M. Kshirsagar, Pravin Ramdas Tak, Tan Kuan Lalit, Keshav Upreti, Kamal Jain, Rituraj School of Computing Srm Institute of Science and Technology Dept. of Computing Technologies Chennai India J.D College of Engg & Mgmt. Department of Electronics Engg. MS Nagpur India Singapore Institute of Technology Singapore Manipal Institute of Technology Dept. of Computer Science & Engineering Karnataka Manipal India Christ University Department of Computer Science Delhi NCR Ghaziabad India Marwadi University Department of Info. Tech. Gujarat Rajkot India

ISBN: (纸本)9798331527495

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. © 2025 ieee.

关键词： Machine vision

来源：评论

学校读者我要写书评

暂无评论

Pre-capture Privacy via Adaptive Single-Pixel Imaging

Pre-capture Privacy via Adaptive Single-Pixel Imaging

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yoko Sogabe Shiori Sugimoto Ayumi Matsumoto Masaki Kitahara NTT Corporation Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As cameras become ubiquitous in our living environment, invasion of privacy is becoming a significant concern. A common approach to privacy preservation is to remove personally identifiable information from a captured image, but there is a risk of the original image being leaked. In this paper, we propose a pre-capture privacy-aware imaging method that captures images from which the details of a pre-specified anonymized target have been eliminated. The proposed method applies a single-pixel imaging frame-work in which we introduce a feedback mechanism called an aperture pattern generator (APG). The introduced APG adaptively outputs the next aperture pattern to avoid sampling the anonymized target by using already acquired data as a clue. Furthermore, the anonymized target can be set to any object without changing hardware. Except for the removed detailed features of the anonymized target, the captured images are of comparable quality to those captured by a general camera and can be used for various computer vision applications. We target faces and license plates and experimentally show that the proposed method can capture clear images in which detailed features of the anonymized target are eliminated, achieving both privacy and utility.

关键词： Privacy computer vision Apertures Cameras Hardware Generators Sensors License plate recognition Faces Identification of persons

来源：评论

学校读者我要写书评

暂无评论

Advancing the Understanding and Evaluation of AR-Generated Scenes: When vision-Language Models Shine and Stumble

Advancing the Understanding and Evaluation of AR-Generated S...

引用

Virtual Reality and 3D User Interfaces Abstracts and workshops (VRW), ieee conference on

作者： Lin Duan Yanming Xiu Maria Gorlatova Department of Electrical and Computer Engineering Duke University

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Augmented Reality (AR) enhances the real world by integrating virtual content, yet ensuring the quality, usability, and safety of AR experiences presents significant challenges. Could vision-Language Models (VLMs) offer a solution for the automated evaluation of AR-generated scenes? In this study, we evaluate the capabilities of three state-of-the-art commercial VLMs—GPT, Gemini, and Claude—in identifying and describing AR scenes. For this purpose, we use DiverseAR, the first AR dataset specifically designed to assess VLMs’ ability to analyze virtual content across a wide range of AR scene complexities. Our findings demonstrate that VLMs are generally capable of perceiving and describing AR scenes, achieving a True Positive Rate (TPR) of up to 93% for perception and 71% for description. While they excel at identifying obvious virtual objects, such as a glowing apple, they struggle when faced with seamlessly integrated content, such as a virtual pot with realistic shadows. Our results highlight both the strengths and the limitations of VLMs in understanding AR scenarios. We identify key factors affecting VLM performance, including virtual content placement, rendering quality, and physical plausibility. This study underscores the potential of VLMs as tools for evaluating the quality of AR experiences.

关键词： Solid modeling Three-dimensional displays conferences User interfaces Rendering (computer graphics) Safety Complexity theory Object recognition Usability Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Enriching Local patterns with Multi-Token Attention for Broad-Sight Neural Networks

Enriching Local Patterns with Multi-Token Attention for Broa...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Hankyul Kang Jongbin Ryu Ajou University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In neural networks, recognizing visual patterns is challenging because global average pooling disregards local patterns and solely relies on over-concentrated activation. Global average pooling enforces the network to learn objects regardless of their location, so features tend to be activated only in specific regions. To support this claim, we provide a novel analysis of the problems that over-concentration brings about in networks with extensive experiments. We analyze the over-concentration through problems arising from feature variance and dead neurons that are not activated. Based on our analysis, we introduce a multi-token attention pooling layer to alleviate the over-concentration problem. Our attention-pooling layer captures broad-sight local patterns by learning multiple tokens with the proposed distillation algorithm. It resolves the high bias and high variance errors of learned multi-tokens, which is crucial when aggregating local patterns with multi-tokens. Our method applies to various vision tasks and network architectures such as CNN, ViT, and MLP-Mixer. The proposed method improves baselines with few extra resources, and a network employing our pooling method works favorably against state-of-the-art networks. We open-source the code at https://***/Lab-LVM/imagenet-models.

关键词： Visualization computer vision Codes Neurons Object detection Network architecture pattern recognition Object recognition Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

PROCEEDINGS - CVPR '85, ieee computer SOCIETY conference ON computer vision AND pattern recognition.

PROCEEDINGS - CVPR '85, IEEE COMPUTER SOCIETY CONFERENCE ON ...

引用

Proceedings - CVPR '85, ieee computer Society conference on computer vision and pattern recognition.

作者： Anon

ISBN: (纸本)0818606339

The following topics are dealt with: edge and boundary analysis;vision systems;motion;shape and 2-D description;stereo and 3-D description;pattern recognition;3-D models;architectures;vision models and texture;image segmentation;applications and parallel algorithms;3-D analysis;contour analysis;character recognition;3-D descriptions from multiple views;and parallel architectures for image processing. 123 papers were presented, of which 121 are published in full in the present proceedings.

关键词： pattern recognition

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500页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：