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

23,134 篇 会议
91 篇 期刊文献
15 册 图书

馆藏范围

23,239 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,630 篇 工学
- 11,162 篇 计算机科学与技术...
- 3,337 篇 软件工程
- 2,414 篇 机械工程
- 1,662 篇 光学工程
- 1,204 篇 电气工程
- 972 篇 控制科学与工程
- 738 篇 信息与通信工程
- 381 篇 仪器科学与技术
- 322 篇 生物工程
- 239 篇 生物医学工程（可授...
- 188 篇 电子科学与技术（可...
- 109 篇 化学工程与技术
- 104 篇 安全科学与工程
- 99 篇 测绘科学与技术
- 85 篇 建筑学
- 82 篇 土木工程
- 82 篇 交通运输工程
- 56 篇 力学（可授工学、理...
3,696 篇 医学
- 3,684 篇 临床医学
- 76 篇 基础医学(可授医学...
3,137 篇 理学
- 1,880 篇 物理学
- 1,604 篇 数学
- 547 篇 统计学（可授理学、...
- 466 篇 生物学
- 243 篇 系统科学
- 107 篇 化学
491 篇 管理学
- 290 篇 图书情报与档案管...
- 212 篇 管理科学与工程(可...
- 74 篇 工商管理
252 篇 艺术学
- 251 篇 设计学（可授艺术学...
58 篇 法学
38 篇 农学
25 篇 教育学
19 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,395 篇 computer vision
3,893 篇 pattern recognit...
3,101 篇 training
2,104 篇 computational mo...
1,897 篇 visualization
1,799 篇 cameras
1,487 篇 feature extracti...
1,475 篇 three-dimensiona...
1,464 篇 shape
1,447 篇 image segmentati...
1,287 篇 robustness
1,234 篇 computer archite...
1,213 篇 semantics
1,112 篇 benchmark testin...
1,111 篇 conferences
1,104 篇 layout
1,093 篇 object detection
1,085 篇 computer science
1,026 篇 codes
907 篇 face recognition

机构

137 篇 univ sci & techn...
124 篇 univ chinese aca...
121 篇 chinese univ hon...
108 篇 tsinghua univers...
108 篇 carnegie mellon ...
105 篇 microsoft resear...
97 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 university of sc...
84 篇 zhejiang univers...
81 篇 shanghai ai lab ...
79 篇 university of ch...
75 篇 shanghai jiao to...
69 篇 microsoft res as...
68 篇 alibaba grp peop...
66 篇 adobe research
65 篇 national laborat...
64 篇 peking univ peop...
61 篇 univ oxford oxfo...
59 篇 peng cheng labor...

作者

80 篇 van gool luc
71 篇 timofte radu
65 篇 zhang lei
43 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
34 篇 li stan z.
33 篇 liu yang
33 篇 xiaoou tang
33 篇 murino vittorio
33 篇 chen chen
33 篇 qi tian
33 篇 li fei-fei
32 篇 tian qi
32 篇 sun jian
30 篇 ying shan
30 篇 pascal fua
29 篇 darrell trevor
28 篇 li xin
28 篇 hanqing lu

语言

23,161 篇 英文
52 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

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

共 23240 条记录，以下是101-110 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：

A Comparative Analysis of OCR Models on Diverse Datasets: Insights from Memes and Hiertext Dataset

A Comparative Analysis of OCR Models on Diverse Datasets: In...

引用

ieee Winter Applications and computer vision Workshops (WACVW)

作者： Iknoor Singh Miguel Colom Kalina Bontcheva University of Sheffield UK Centre Borelli ENS Paris-Saclay

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Optical Character recognition (OCR) plays a critical role in various text extraction applications, yet its performance varies significantly across languages, visual appearances, and document types. This study benchmarks several state-of-the-art OCR models on two distinct and challenging datasets: MemeDataset, which contains informal, meme-based images, and HierText, featuring multilingual text in natural scenes and documents. We comprehensively evaluate a range of OCR models on these datasets, highlighting their strengths and limitations. While large multi-modal models excel in complex text extraction, they are hindered by slower processing times and irrelevant text generation in challenging scenarios. Also, we examine the models' ability to handle multilingual content, finding that some newer models perform well in certain languages but struggle in others. Our results emphasise the need for further enhancements in open-sourced large multimodal models to improve efficiency and accuracy, particularly in multilingual scenarios. In contrast, other open-source models such as EasyOCR deliver faster inference times with reasonably competitive performance, making them more suitable for time-sensitive applications. The findings offer valuable insights for researchers and practitioners in selecting the right models for diverse use cases.

关键词： Analytical models Visualization computer vision Accuracy Computational modeling conferences Optical character recognition Multilingual Internet Optimization

来源：评论

学校读者我要写书评

暂无评论

FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection

FoundPAD: Foundation Models Reloaded for Face Presentation A...

引用

ieee Winter Applications and computer vision Workshops (WACVW)

作者： Guray Ozgur Eduarda Caldeira Tahar Chettaoui Fadi Boutros Raghavendra Ramachandra Naser Damer Fraunhofer IGD Germany Norwegian University of Science and Technology (NTNU) Norway TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need for generalizable presentation attack detection (PAD) algorithms. Current PAD solutions suffer from two main problems: low generalization to unknown scenarios and large training data requirements. Foundation models (FM) are pretrained on extensive datasets, achieving remarkable results when generalizing to unseen domains and allowing for efficient task-specific adaption even in low data availability settings. This is one of the first works to recognize the potential of FMs and adapt them for the downstream task of PAD. The FM under consideration is adapted with LoRA weights while simultaneously training a classification header. The resultant architecture, FoundPAD, is highly generalizable to unseen domains, achieving competitive results in several settings under different data availability scenarios and even when using synthetic training data. To encourage reproducibility and facilitate further research in PAD, we publicly release the implementation of FoundPAD at https://***/gurayozgur/FoundPAD.

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

来源：评论

学校读者我要写书评

暂无评论

CLIPping Imbalances: A Novel Evaluation Baseline and PEARL Dataset for Pedestrian Attribute recognition

CLIPping Imbalances: A Novel Evaluation Baseline and PEARL D...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Kamalakar Vijay Thakare Lalit Lohani Kamakshya Prasad Nayak Debi Prosad Dogra Heeseung Choi Hyungjoo Jung Ig-Jae Kim Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul Republic of Korea Indian Institute of Technology Bhubaneswar Odisha India Yonsei-KIST Convergence Research Institute Yonsei University Seoul Republic of Korea

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Pedestrian Attribute recognition (PAR) serves as a fun-damental task in computer vision and is crucial for upgradign security systems. It helps in precisely identifying and characterizing various attributes of pedestrians. However, current PAR datasets have certain issues in representing a wide range of attributes correctly, which makes the ex-isting PAR methods less effective in real-world scenarios. Addressing this limitation, this paper introduces PEARL, a comprehensive dataset comprising of diverse pedestrian images annotated with 146 attributes. These samples have been sourced from surveillance videos across twelve coun-tries. This paper also formulates an image-based PAR using language-image fusion strategy and utilizes CLIP as a new evaluation baseline. Specifically, we leverage textual infor-mation by transforming sets of attributes into meaningful sentences. Addressing the inherent data imbalance in PAR, we provide three types of prompt settings to optimize the training of the CLIP model. Our evaluation encompasses a thorough assessment of the proposed baseline model across various datasets, including PEARL dataset as well as estab-lished PAR benchmarks such as PA100K, RAP, and PETA.

关键词： Training computer vision Pedestrians Computational modeling Surveillance Contrastive learning Benchmark testing Data models Security Videos

来源：评论

学校读者我要写书评

暂无评论

Online-LoRA: Task-Free Online Continual Learning via Low Rank Adaptation

Online-LoRA: Task-Free Online Continual Learning via Low Ran...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Xiwen Wei Guihong Li Radu Marculescu The University of Texas at Austin

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for nonstationary data streams that do not have well-defined task boundaries. This challenge is exacerbated by the memory constraints and privacy concerns inherent in rehearsal buffers. To tackle catastrophic forgetting, in this paper, we introduce Online-LoRA, a novel framework for task-free OCL. Online-LoRA allows to finetune pre-trained vision Transformer (ViT) models in real-time to address the limitations of rehearsal buffers and leverage pre-trained models' performance benefits. As the main contribution, our approach features a novel online weight regularization strategy to identify and consolidate important model parameters. Moreover, Online-LoRA leverages the training dynamics of loss values to enable the automatic recognition of the data distribution shifts. Extensive experiments across many task-free OCL scenarios and benchmark datasets (including CIFAR-100, ImageNet-R, ImageNet-S, CUB-200 and CORe50) demonstrate that Online-LoRA can be robustly adapted to various ViT architectures, while achieving better performance compared to SOTA methods 1 1 Code: https://***/Christina200/***.

关键词： Continuing education Training computer vision Adaptation models Privacy Upper bound Memory management Transformers Real-time systems Streams

来源：评论

学校读者我要写书评

暂无评论

ieee computer Society conference on computer vision and pattern recognition Workshops

IEEE Computer Society Conference on Computer Vision and Patt...

引用

31st Meeting of the ieee/cvf conference on computer vision and pattern recognition Workshops, CVPRW 2018

ISBN: (纸本)9781538661000

The proceedings contain 307 papers. The topics discussed include: deep features for recognizing disguised faces in the wild;unconstrained fingerphoto database;hybrid user-independent and user-dependent offline signature verification with a two-channel CNN;it takes two to tango: cascading off-the-shelf face detectors;time analysis of pulse-based face anti-spoofing in visible and NIR;a deep face identification network enhanced by facial attributes prediction;gait recognition by deformable registration;and fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Focusing on what to Decode and what to Train: SOV Decoding with Specific Target Guided DeNoising and vision Language Advisor

Focusing on what to Decode and what to Train: SOV Decoding w...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Junwen Chen Yingcheng Wang Keiji Yanai Department of Informatics The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOID) task by leveraging the detection of DETR and the prior knowledge of vision-Language Model (VLM). However, these methods suffer from extended training times and complex optimization due to the entanglement of object detection and HOI recognition during the decoding process. Especially, the query embeddings used to predict both labels and boxes suffer from ambiguous representations, and the gap between the prediction of HOI labels and verb labels is not considered. To address these challenges, we introduce SOV-STG-VLA with three key components: Subject-Object-Verb (SOV) decoding, Specific Target Guided (STG) denoising, and a vision-Language Advisor (VLA). Our SOV decoders disentangle object detection and verb recognition with a novel interaction region representation. The STG denoising strategy learns label embeddings with ground-truth information to guide the training and inference. Our SOV-STG achieves a fast convergence speed and high accuracy and builds a foundation for the VLA to incorporate the prior knowledge of the VLM. We introduce a vision advisor decoder to fuse both the interaction region information and the VLM's vision knowledge and a Verb-HOI prediction bridge to promote interaction representation learning. Our VLA notably improves our SOV-STG and achieves SOTA performance with one-sixth of training epochs compared to recent SOTA. Code and models are available at https://***/cjw2021/SOV-STG-VLA.

关键词： Training Bridges Representation learning Visualization Noise reduction Object detection Transformers Decoding Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large vision-Language Model via Causality Analysis

Who Brings the Frisbee: Probing Hidden Hallucination Factors...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Po-Hsuan Huang Jeng-Lin Li Chin-Po Chen Ming-Ching Chang Wei-Chao Chen Inventec Corporation Taipei City University at Albany - SUNY Albany New York

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural language. How-ever, a major challenge in their real-world application is hallucination, where LVLMs generate non-existent visual elements, eroding user trust. The underlying mechanism driving this multimodal hallucination is poorly understood. Minimal research has illuminated whether contexts such as sky, tree, or grass field involve the LVLM in hallucinating a frisbee. We hypothesize that hiddenfactors, such as objects, contexts, and semantic foreground-background structures, induce hallucination. This study proposes a novel causal approach: a hallucination probing system to identify these hidden factors. By analyzing the causality between images, text prompts, and network saliency, we systematically ex-plore interventions to block these factors. Our experimen-tal findings show that a straightforward technique based on our analysis can significantly reduce hallucinations. Additionally, our analyses indicate the potential to edit network internals to minimize hallucinated outputs.

关键词： Training Visualization computer vision Prevention and mitigation Computational modeling Semantics Natural languages Cause effect analysis Object recognition Testing

来源：评论

学校读者我要写书评

暂无评论

CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face recognition

CLFace: A Scalable and Resource-Efficient Continual Learning...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Md Mahedi Hasan Shoaib Meraj Sami Nasser Nasrabadi West Virginia University Morgantown West Virginia USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training of existing deep neural network-based FR algorithms, which are pre-trained offline on large-scale stationary datasets, encounter two major challenges: (I) catastrophic forgetting of previously learned identities, and (II) the need to store past data for complete retraining from scratch, leading to significant storage constraints and privacy concerns. In this paper, we introduce CLFace, a continual learning framework designed to preserve and incrementally extend the learned knowledge. CLFace eliminates the classification layer, resulting in a resource-efficient FR model that remains fixed throughout lifelong learning and provides label-free supervision to a student model, making it suitable for open-set face recog-nition during incremental steps. We introduce an objective function that employs feature-level distillation to reduce drift between feature maps of the student and teacher models across multiple stages. Additionally, it incorpo-rates a geometry-preserving distillation scheme to maintain the orientation of the teacher model's feature embedding. Furthermore, a contrastive knowledge distillation is incor-porated to continually enhance the discriminative power of the feature representation by matching similarities between new identities. Experiments on several benchmark FR datasets demonstrate that CLFace outperforms baseline approaches and state-of-the-art methods on unseen identities using both in-domain and out-of-domain datasets.

关键词： Training Continuing education Data privacy computer vision Face recognition Benchmark testing Linear programming

来源：评论

学校读者我要写书评

暂无评论

Neural SDF for Shadow-Aware Unsupervised Structured Light

Neural SDF for Shadow-Aware Unsupervised Structured Light

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Kazuto Ichimaru Diego Thomas Takafumi Iwaguchi Hiroshi Kawasaki Kyushu University Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Among various active 3D measurement techniques, Structured Light (SL) is one of the most popular methods for its robustness and high accuracy. The ordinary SL system consists of a camera and a projector, and by projecting a pre-defined pattern, we can obtain pixel-to-pixel correspondences between the camera and the projector for triangulation. However, if we lack knowledge of the projected pattern for some reason, e.g., the projected pattern is not as expected due to lens distortion, inaccurate calibration, undesired optical phenomena like inter-reflection, and so on, the accuracy of conventional SL is severely degraded. As a remedy, we propose unsupervised structured light (USSL), which does not explicitly use prior knowledge of the pattern. Inspired by the fact that humans can recognize the scene structure illuminated by an unknown light source (e.g. rotating mirror ball), and some prior works have succeeded in novel-view-synthesis under unknown illumination conditions, we implement USSL on Neural Signed Distance Fields (Neural SDF) pipeline with implicit reflection module powered by a neural network. Additionally, since every SL method causes occlusion (shadow) by pattern projection, we must consider it for accurate shape reconstruction. To this end, we integrate shadow volume rendering into the proposed pipeline. Experiments with synthetic and real datasets are conducted to confirm the feasibility of the proposed method.

关键词： Accuracy Three-dimensional displays Shape Pipelines Optical distortion Rendering (computer graphics) Cameras Robustness Reflection Optical reflection

来源：评论

学校读者我要写书评

暂无评论

Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation

Pay Attention to Your Neighbours: Training-Free Open-Vocabul...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Sina Hajimiri Ismail Ben Ayed Jose Dolz ÉTs Montreal

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Despite the significant progress in deep learning for dense visual recognition problems, such as semantic segmentation, traditional methods are constrained by fixed class sets. Meanwhile, vision-language foundation models, such as CLIP, have showcased remarkable effectiveness in numerous zero-shot image-level tasks, owing to their robust generalizability. Recently, a body of work has investigated utilizing these models in open-vocabulary semantic segmentation (OVSS). However, existing approaches often rely on impractical supervised pretraining or access to additional pretrained networks. In this work, we propose a strong baseline for training-free OVSS, termed Neighbour-Aware CLIP (NACLIP), representing a straightforward adaptation of CLIP tailored for this scenario. Our method enforces localization of patches in the self-attention of CLIP's vision transformer which, despite being crucial for dense prediction tasks, has been overlooked in the OVSS literature. By incorporating design choices favouring segmentation, our approach significantly improves performance without requiring additional data, auxiliary pretrained networks, or extensive hyperparameter tuning, making it highly practicalfor real-world applications. Experiments are performed on 8 popular semantic segmentation benchmarks, yielding state-of-the-art performance on most scenarios. Our code is publicly available at https://***/sinahmr/NACLIP.

关键词： Location awareness Training Visualization computer vision Semantic segmentation Semantics Benchmark testing Predictive models Transformers Tuning

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 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：