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

29,390 篇 会议
1,396 册 图书
224 篇 期刊文献

馆藏范围

31,008 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

17,278 篇 工学
- 13,621 篇 计算机科学与技术...
- 5,199 篇 软件工程
- 2,971 篇 机械工程
- 2,644 篇 光学工程
- 1,411 篇 控制科学与工程
- 1,408 篇 电气工程
- 1,334 篇 信息与通信工程
- 656 篇 生物工程
- 578 篇 仪器科学与技术
- 513 篇 生物医学工程（可授...
- 466 篇 电子科学与技术（可...
- 251 篇 化学工程与技术
- 212 篇 安全科学与工程
- 140 篇 交通运输工程
- 132 篇 建筑学
- 121 篇 材料科学与工程（可...
- 119 篇 土木工程
5,057 篇 理学
- 3,128 篇 物理学
- 2,405 篇 数学
- 824 篇 生物学
- 802 篇 统计学（可授理学、...
- 300 篇 系统科学
- 228 篇 化学
3,831 篇 医学
- 3,799 篇 临床医学
- 186 篇 基础医学(可授医学...
- 140 篇 药学(可授医学、理...
1,060 篇 管理学
- 617 篇 图书情报与档案管...
- 468 篇 管理科学与工程(可...
- 145 篇 工商管理
373 篇 艺术学
- 373 篇 设计学（可授艺术学...
116 篇 法学
81 篇 农学
48 篇 教育学
43 篇 经济学
18 篇 军事学
8 篇 文学

主题

12,594 篇 computer vision
5,697 篇 pattern recognit...
3,181 篇 training
2,264 篇 cameras
2,178 篇 computational mo...
2,116 篇 feature extracti...
2,049 篇 image segmentati...
1,971 篇 visualization
1,967 篇 shape
1,642 篇 robustness
1,491 篇 layout
1,476 篇 three-dimensiona...
1,444 篇 computer science
1,338 篇 computer archite...
1,295 篇 object detection
1,220 篇 semantics
1,142 篇 face recognition
1,107 篇 conferences
1,077 篇 benchmark testin...
1,056 篇 humans

机构

137 篇 univ sci & techn...
134 篇 tsinghua univers...
134 篇 univ chinese aca...
118 篇 chinese univ hon...
101 篇 microsoft resear...
97 篇 zhejiang univers...
94 篇 national laborat...
93 篇 shanghai jiao to...
93 篇 zhejiang univ pe...
85 篇 university of sc...
79 篇 shanghai ai lab ...
78 篇 swiss fed inst t...
65 篇 microsoft res as...
62 篇 adobe research
62 篇 computer vision ...
61 篇 peking univ peop...
58 篇 univ oxford oxfo...
57 篇 google mountain ...
57 篇 hong kong univ s...
56 篇 google res mount...

作者

107 篇 umapada pal
81 篇 van gool luc
68 篇 zhang lei
59 篇 timofte radu
41 篇 yang yi
37 篇 loy chen change
37 篇 hanqing lu
33 篇 liu yang
33 篇 xiaoou tang
32 篇 nassir navab
32 篇 wang liang
30 篇 tian qi
29 篇 h. bischof
29 篇 jan-michael frah...
29 篇 vittorio murino
29 篇 darrell trevor
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 murino vittorio
27 篇 chen chen

语言

30,873 篇 英文
89 篇 中文
67 篇 其他
6 篇 土耳其文
2 篇 日文
2 篇 俄文
1 篇 西班牙文

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

共 31010 条记录，以下是4701-4710 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：

Multi-modal Aerial View Image Challenge: SAR Classification

Multi-modal Aerial View Image Challenge: SAR Classification

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH Ecuador Computer Vision Center ESPOL Polytechnic University Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities, the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years, the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration, the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognition (UNICORN) dataset alongside a revised competition format, focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures, alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

关键词： Measurement Laser radar Image recognition Pipelines Optical imaging Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Ima...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gihyun Kwon Simon Jenni Dingzeyu Li Joon-Young Lee Jong Chul Ye Fabian Caba Heilbron KAIST Adobe

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.

关键词： computer vision Fuses Semantics Text to image Diffusion models pattern recognition Optimization

来源：评论

学校读者我要写书评

暂无评论

Adversarial Score Distillation: When Score Distillation Meets GAN

Adversarial Score Distillation: When Score Distillation Meet...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Min Wei Jingkai Zhou Junyao Sun Xuesong Zhang Beijing University of Posts and Telecommunications South China University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale, manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones. To explain and analyze these issues, we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm, we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization, resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD), which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore, to explore the generalization ability of our paradigm, we extend ASD to the image editing task, which achieves competitive results. The project page and code are at this link.

关键词： computer vision Codes Computational modeling Generative adversarial networks pattern recognition Optimization

来源：评论

学校读者我要写书评

暂无评论

Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers

Siamese Natural Language Tracker: Tracking by Natural Langua...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Feng, Qi Ablavsky, Vitaly Bai, Qinxun Sclaroff, Stan Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU. Code for this work is available at https: // github. com/ fredfung007/ snlt.

关键词： Visualization computer vision Natural languages Graphics processing units computer architecture Benchmark testing Real-time systems

来源：评论

学校读者我要写书评

暂无评论

On Training Sketch Recognizers for New Domains

On Training Sketch Recognizers for New Domains

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yesilbek, Kemal Tugrul Sezgin, T. Metin Beat Res BV Amsterdam Netherlands Koc Univ Istanbul Turkey

ISBN: (纸本)9781665448994

Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific recognizers. This gives rise to two fundamental concerns: First, will the data collection protocol yield ecologically valid data? Second, will the amount of collected data suffice to train sufficiently accurate classifiers? In this paper, we draw attention to these two concerns. We show that the ecological validity of the data collection protocol and the ability to accommodate small datasets are significant factors impacting recognizer accuracy in realistic scenarios. More specifically, using sketch-based gaming as a use case, we show that deep learning methods, as well as more traditional methods, suffer significantly from dataset shift. Furthermore, we demonstrate that in realistic scenarios where data is scarce and expensive, standard measures taken for adapting deep learners to small datasets fall short of comparing favorably with alternatives. Although transfer learning, and extensive data augmentation help deep learners, they still perform significantly worse compared to standard setups (e.g., SVMs and GBMs with standard feature representations). We pose learning from small datasets as a key problem for the deep sketch recognition field, one which has been ignored in the bulk of the existing literature.

关键词： Training Deep learning computer vision Protocols conferences Transfer learning Data collection

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Visual Attribute Extraction from Fashion Wear

Fine-Grained Visual Attribute Extraction from Fashion Wear

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Parekh, Viral Shaik, Karimulla Biswas, Soma Chelliah, Muthusamy Flipkart Internet Private Ltd Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Automatically extracting visual attributes for e-commerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.

关键词： Visualization computer vision conferences Footwear computer architecture Neck Data mining

来源：评论

学校读者我要写书评

暂无评论

Person Re-identification using Heterogeneous Local Graph Attention Networks

Person Re-identification using Heterogeneous Local Graph Att...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhong Zhang, Haijia Liu, Shuang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China

ISBN: (纸本)9781665445092

Recently, some methods have focused on learning local relation among parts of pedestrian images for person reidentification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation among parts from different images, which results in incomplete local relation information. In this paper, we propose a novel deep graph model named Heterogeneous Local Graph Attention Networks (HLGAT) to model the inter-local relation and the intra-local relation in the completed local graph, simultaneously. Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features. As for the inter-local relation, we propose the attention regularization loss to constrain the attention weights based on the identities of local features in order to describe the inter-local relation accurately. As for the intra-local relation, we propose to inject the contextual information into the attention weights to consider structure information. Extensive experiments on Market-1501, CUHK03, DukeMTMC-reID and MSMT17 demonstrate that the proposed HLGAT outperforms the state-of-the-art methods.

关键词： computer vision Aggregates pattern recognition Context modeling

来源：评论

学校读者我要写书评

暂无评论

SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field

SurroundSDF: Implicit 3D Scene Understanding Based on Signed...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lizhe Liu Bohua Wang Hongwei Xie Daqi Liu Li Liu Zhiqiang Tian Kuiyuan Yang Bing Wang Xiaomi EV School of Software Engineering Xi'an Jiaotong University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision-centric 3D environment understanding is both vi-tal and challenging for autonomous driving systems. Re-cently, object-free methods have attracted considerable at-tention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end, in this paper, we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically, we introduce a query-based approach and utilize SDF con-strained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore, considering the absence of precise SDF ground truth, we propose a novel weakly supervised paradigm for SDF, referred to as the Sandwich Eikonal formulation, which emphasizes applying correct and dense constraints on both sides of the surface, thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

关键词： Surface reconstruction computer vision Three-dimensional displays Accuracy Semantics Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Every Corporation Owns Its Structure: Corporate Credit Rating via Graph Neural Networks 5th

Every Corporation Owns Its Structure: Corporate Credit Ratin...

引用

5th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Feng, Bojing Xu, Haonan Xue, Wenfang Xue, Bindang Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beihang Univ Sch Astronaut Beijing Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.

关键词： Corporate credit rating Financial risk Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling

AttentiveNAS: Improving Neural Architecture Search via Atten...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Dilin Li, Meng Gong, Chengyue Chandra, Vikas Facebook Menlo Pk CA 94025 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models. While uniform sampling has been widely used for its simplicity, it is agnostic of the model performance Pareto front, which is the main focus in the search process, and thus, misses opportunities to further improve the model accuracy. In this work, we propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto. We also propose algorithms to efficiently and effectively identify the networks on the Pareto during training. Without extra re-training or post-processing, we can simultaneously obtain a large number of networks across a wide range of FLOPs. Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS, Once-for-All networks and FBNetV3. We also achieve ImageNet accuracy of 80.1% with only 491 MFLOPs. Our training code and pretrained models are available at https://***/facebookresearch/AttentiveNAS.

关键词： Training computer vision Codes computer architecture 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 << < 467 468 469 470 471 472 473 474 475 476 > >>

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：