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

1,535 篇 会议
21 册 图书
13 篇 期刊文献

馆藏范围

1,569 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,449 篇 工学
- 1,046 篇 计算机科学与技术...
- 655 篇 机械工程
- 358 篇 电气工程
- 111 篇 软件工程
- 55 篇 控制科学与工程
- 49 篇 信息与通信工程
- 35 篇 光学工程
- 28 篇 仪器科学与技术
- 17 篇 网络空间安全
- 15 篇 生物工程
- 9 篇 生物医学工程（可授...
- 8 篇 电子科学与技术（可...
- 8 篇 安全科学与工程
- 7 篇 测绘科学与技术
- 4 篇 化学工程与技术
- 3 篇 建筑学
112 篇 医学
- 108 篇 临床医学
- 6 篇 基础医学(可授医学...
- 4 篇 特种医学
87 篇 理学
- 38 篇 物理学
- 34 篇 数学
- 24 篇 生物学
- 12 篇 统计学（可授理学、...
- 9 篇 系统科学
- 5 篇 化学
42 篇 管理学
- 32 篇 图书情报与档案管...
- 10 篇 管理科学与工程(可...
24 篇 艺术学
- 24 篇 设计学（可授艺术学...
19 篇 法学
- 19 篇 社会学
15 篇 文学
- 15 篇 新闻传播学
2 篇 教育学
2 篇 农学
1 篇 经济学

主题

528 篇 computer vision
139 篇 face recognition
135 篇 pattern recognit...
135 篇 feature extracti...
130 篇 cameras
129 篇 image segmentati...
128 篇 object recogniti...
115 篇 object detection
103 篇 training
98 篇 robustness
94 篇 images
92 篇 image recognitio...
90 篇 shape
90 篇 layout
84 篇 visualization
77 篇 image reconstruc...
76 篇 humans
70 篇 computer science
66 篇 computational mo...
66 篇 image classifica...

机构

23 篇 rhein westfal th...
18 篇 carnegie mellon ...
16 篇 chinese acad sci...
15 篇 stanford univ st...
14 篇 univ chinese aca...
11 篇 australian natl ...
11 篇 univ calif berke...
10 篇 univ sci & techn...
10 篇 mit cambridge ma...
10 篇 univ maryland co...
9 篇 microsoft res as...
9 篇 univ washington ...
8 篇 microsoft resear...
8 篇 microsoft res re...
8 篇 natl univ singap...
7 篇 chinese univ hon...
7 篇 tech univ munich...
6 篇 mpi intelligent ...
6 篇 kaust thuwal
6 篇 imperial coll lo...

作者

20 篇 leibe bastian
12 篇 ji qiang
10 篇 li stan z.
9 篇 pal umapada
8 篇 zhang lei
8 篇 chellappa rama
8 篇 lei zhen
8 篇 zafeiriou stefan...
8 篇 hu weiming
8 篇 hua gang
7 篇 ramanan deva
7 篇 wang xiaogang
7 篇 yan shuicheng
7 篇 mubarak shah
6 篇 van gool luc
6 篇 darrell trevor
6 篇 luiten jonathon
6 篇 pollefeys marc
6 篇 bischof horst
6 篇 ghanem bernard

语言

1,554 篇 英文
9 篇 土耳其文
2 篇 日文
2 篇 其他
1 篇 西班牙文
1 篇 中文

检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是141-150 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：

26th International conference on pattern recognition, ICPR 2022

26th International Conference on Pattern Recognition, ICPR 2...

引用

26th International conference on pattern recognition, ICPR 2022

ISBN: (纸本)9783031377419

the proceedings contain 49 papers. the special focus in this conference is on pattern recognition. the topics include: Towards Content-Aware Pixel-Wise Comic Panel Segmentation;data-Driven Transform Based Adaptive Compressive Sensing Framework for 3D Reconstruction of Forests;Land Cover Classification for Fires Using Sentinel-2 Satellite RGB Images and Deep Transfer Learning;landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco;flying Wing Wildfire Tracking Using Visual Servoing and Gimbal Control;An Overview of Tools and Algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR Data;Towards the Automation of Wildfire Monitoring with Aerial Vehicles: the FIREFRONT Project;real-Time Georeferencing of Fire Front Aerial Images Using Structure from Motion and Iterative Closest Point;forest Fires Identification Using Self-Supervised Learning;georeferencing High-Altitude Aerial Imagery;Wildfires Detection and Segmentation Using Deep CNNs and vision Transformers;a Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line Dataset for Manga109;towards Multi-class Forest Floor Analysis;combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection;towards Unconstrained Audio Splicing Detection and Localization with Neural Networks;comprint: Image Forgery Detection and Localization Using Compression Fingerprints;H4VDM: H.264 Video Device Matching;GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection;Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis;attacking and Defending Printer Source Attribution Classifiers in the Physical Domain;descriptive Algorithmic Schemes as Image Analysis Machine Programs;application of Artificial Intelligence in Ophthalmology for Coagulate Map Formation to Carry Out Laser Eye Treatment;considering Meanings and Effects of Frames Without Onomatopoeias in Japanese Comics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

From Points to Multi-Object 3D Reconstruction

From Points to Multi-Object 3D Reconstruction

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Engelmann, Francis Rematas, Konstantinos Leibe, Bastian Ferrari, Vittorio Rhein Westfal TH Aachen Aachen Germany Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a method to detect and reconstruct multiple 31) objects from a single RGB image. the key idea is to optimize for detection, alignment and shape jointly over all objects in the RGB image, while focusing on realistic and physically plausible reconstructions. 'lb this end, we propose a key-point detector that localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes - all in a single forward pass. the proposed method formulates 3D shape reconstruction as a shape selection problem, i.e. it selects among exemplar shapes from a given database. this makes it agnostic to shape representations, which enables a lightweight reconstruction of realistic and visually-pleasing shapes based on CAD-models, while the training objective is formulated around point clouds and voxel representations. A collision-loss promotes non-intersecting objects, further increasing the reconstruction realism. Given the RGB image, the presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable. Our experiments compare multiple approaches for 9-DoF bounding box estimation, evaluate the novel shape-selection mechanism and compare to recent methods in terms of 3D bounding box estimation and 3D shape reconstruction quality.

关键词： Training Solid modeling three-dimensional displays Shape Databases Computational modeling Estimation

来源：评论

学校读者我要写书评

暂无评论

Electronic System for Signage Detection 26

Electronic System for Signage Detection

引用

26th International conference on Electronics (ELECTRONICS)

作者： Molina, Cristian Saeteros, Morelva Iza, Danny Loja, Abraham Salesian Polytech Univ Quito Ecuador Univ Deusto Bizkaia Spain Natl Polytech Sch Quito Ecuador

ISBN: (纸本)9781665483216

the present work focuses on the need to give independence, security and generate a better lifestyle for people with visual disabilities, through their journeys abroad. the work seeks to be an aid so that people with visual disabilities can move better, recognizing useful signage according to standardized colors for prevention, obligation and information, for their safety and performance, such as pedestrian traffic lights, bus stops, pedestrian crossings, in addition, to evaluate the image detection methods using Machine Learning with deep learning techniques through the haar-cascade model to give a better recognition response to the user. the input to the system is a continuous video sequence, which analyzes and provides the user with an audible output by recognizing the different traffic signs at 2 meters. this process is based on an embedded system, which consists of a Raspberry Pi single board computer, Raspberry camera and headphones, the system was designed to be a low cost tool with a rechargeable battery that can be adapted to the white cane for the support and autonomy of people with visual disabilities.

关键词： Artificial vision OpenCV Match template Haar-classifier

来源：评论

学校读者我要写书评

暂无评论

ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alice Baird Alan Cowen Stefanos Zafeiriou Queen Mary University of London UK Hume AI USA Imperial College London UK

the 5th ABAW Competition is part of the respective Workshop held in conjunction with ieee cvpr 2023 and is a continuation of the Competitions held at ECCV 2022, ieee cvpr 2022, ICCV 2021, ieee FG 2020 and cvpr 2017 conferences. It is dedicated at automatically analyzing affect. For this year’s Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. the former database is an audiovisual (A/V) one of around 600 videos of around 3M frames and is annotated for: a) two continuous affect dimensions, valence (how positive/negative a person is) and arousal (how active/passive a person is); b) basic expressions (e.g. happiness, neutral); and c) action units (i.e., facial muscle actions). the latter dataset is A/V in which reactions of individuals to emotional stimuli have been annotated for seven emotional expression intensities. thus the 5th ABAW Competition encompasses four Challenges: i) Valence-Arousal Estimation, ii) Expression Classification, iii) Action Unit Detection, and iv) Emotional Reaction Intensity Estimation. In this paper, we present these Challenges and their corpora, we outline the evaluation metrics and present the baseline systems and top performing teams’ per Challenge along with their obtained performance. More information for the Competition can be found in: https://***/resources/ cvpr-2023-5th-abaw.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation

Alpha-Refine: Boosting Tracking Performance by Precise Bound...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Bin Zhang, Xinyu Wang, Dong Lu, Huchuan Yang, Xiaoyun Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Remark AI London England

ISBN: (纸本)9781665445092

Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage strategy to improve bounding box estimation. these methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. this work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base tracker's performance with little extra latency. the proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (AR strengthened DiMP50) achieve good efficiency-precision trade-off while the ARDiMPsuper (AR strengthened DiMPsuper) achieves very competitive performance at a realtime speed. Code and pretrained models are available at hups://***/MasterBin-IIALI/Alphalteline.

关键词： Couplings Visualization computer vision Target tracking Correlation Computational modeling Estimation

来源：评论

学校读者我要写书评

暂无评论

Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segment...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Junjiao Tian Lavisha Aggarwal Andrea Colaco Zsolt Kira Mar Gonzalez-Franco Georgia Institute of Technology Google

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Producing quality segmentation masks for images is a fundamental problem in computer vision. Recent research has explored large-scale supervised training to enable zero- shot transfer segmentation on virtually any image style and unsupervised training to enable segmentation without dense annotations. However, constructing a model capable of segmenting anything in a zero-shot manner without any anno-tations is still challenging. In this paper, we propose to uti-lize the self-attention layers in stable diffusion models to achieve this goal because the pre-trained stable diffusion model has learned inherent concepts of objects within its attention layers. Specifically, we introduce a simple yet ef-fective iterative merging process based on measuring KL divergence among attention maps to merge them into valid segmentation masks. the proposed method does not re-quire any training or language dependency to extract qual-ity segmentation for any images. On COCO-Stuff-27, our method surpasses the prior unsupervised zero-shot trans-fer SOTA method by an absolute 26% in pixel accuracy and 17% in mean IoU. the project page is at https://***/view/diffseg/home. 1 1 Georgia Institute of Technology

关键词： Training Image segmentation computer vision Satellites Merging Diffusion models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

this paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ieee cvpr 2024. the 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Satellite and Debris Detection: YOLO in Action 26

A Deep Learning Approach for Satellite and Debris Detection:...

引用

26th International conference on computer and Information Technology, ICCIT 2023

作者： Ahamed, Md Younus Bin Syed, Md Asif Chatterjee, Paroma Bin Habib, Al Zadid Sultan West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350359015

Satellite detection refers to the identification and recognition of satellites in satellite images. this task is crucial for various applications, including space surveillance, satellite tracking, and space debris monitoring. Several studies have been conducted to develop methods and techniques for satellite detection. the intricacy and scale of satellite images make satellite detection incredibly challenging. the process of manual identification and classification necessitates a significant amount of time. Recently, Deep Learning (DL) has emerged as a highly promising approach in computer vision (CV) tasks. this study evaluates the effectiveness of three widely employed DL-based object detection algorithms, specifically YOLOv4, YOLOv5, and YOLOv8, within the field of satellite detection. the algorithms have undergone training utilizing a dataset of satellite photos comprising both satellites and debris. the evaluation of the algorithm's performance involved the utilization of broadly accepted metrics, including Precision, Recall, Mean Average Precision (mAP), Box Loss, and Class Loss. the findings indicated that all three algorithms exhibited a high level of accuracy in detecting and precisely determining the location of objects inside satellite data. the performance of YOLOv5 and YOLOv8 surpassed that of YOLOv4 across various measures, with a notable advantage observed in detecting tiny objects. Overall, this study demonstrates the capabilities of DL algorithms in the context of satellite detection and classification techniques. © 2023 ieee.

关键词： Debris Object Detection Satellite YOLOv4 YOLOv5 YOLOv8

来源：评论

学校读者我要写书评

暂无评论

STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack: Template-free Visual Tracking with Space-time Memo...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fu, Zhihong Liu, Qingjie Fu, Zehua Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS.

关键词： Visualization computer vision Target tracking Codes Adaptive systems Benchmark testing Boosting

来源：评论

学校读者我要写书评

暂无评论

A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. the technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. the code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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157页 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：