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

23,000 篇 会议
126 册 图书
92 篇 期刊文献

馆藏范围

23,217 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,622 篇 工学
- 11,107 篇 计算机科学与技术...
- 3,479 篇 软件工程
- 2,445 篇 机械工程
- 1,716 篇 光学工程
- 1,075 篇 电气工程
- 1,014 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
- 86 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...
3,241 篇 理学
- 1,939 篇 物理学
- 1,640 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
3,966 篇 pattern recognit...
3,005 篇 training
2,007 篇 computational mo...
1,818 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
1,092 篇 computer science
1,087 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
61 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

23,137 篇 英文
52 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 23218 条记录，以下是771-780 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：

Network Amplification with Efficient MACs Allocation

Network Amplification with Efficient MACs Allocation

引用

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

作者： Liu, Chuanjian Han, Kai Xiao, An Nie, Ying Zhang, Wei Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracies, such as EfficientNet and RegNet. These works try to enlarge the network architecture with one unified rule by sampling and statistical methods. However, the rule is not prospective to the design of large networks because it is obtained from the experience of researchers on small network architectures. In this paper, we propose to enlarge the capacity of CNN models by fine-grained MACs allocation for the width, depth and resolution on the stage level. In particular, starting from a base small model, we gradually add extra channels, layers or resolution by using a dynamic programming manner. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. On EfficientNet, our method consistently outperforms the performance of the original scaling method. In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies under the setting of 600M and 4.4B MACs, respectively.

关键词： Statistical analysis Computational modeling Heuristic algorithms Neural networks Network architecture pattern recognition Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

引用

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

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Wu, Zongwei Zhou, Zhuyun Chen, Cailian Zhou, Han Timofte, Radu Dong, Wei Tian, Yuqiong Chen, Jun Lul, Xin Zhu, Yurui Wang, Xi Li, Dong Xiao, Jie Zhang, Yunpeng Fu, Xueyang Zha, Zheng-Jun Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Wei, Yanyan Xiaol, Jie Ful, Xueyang Zhal, Zheng-Jun Lu, Xin Zhao, Zhihao Sun, Long Yang, Tingting Pan, Jinshan Tang, Jinhui Dong, Jiangxin Benjdira, Bilel Nassif, Mohammed Koubaa, Anis Elhayek, Ahmed Ali, Anas M. Tokoro, Kyotaro Kawai, Kento Yokoyama, Kaname Seno, Takuya Kondo, Yuki Ukita, Norimichi Li, Chenghua Yang, Bo Wu, Zhiqi Chen, Gao Yu, Yihan Chen, Sixiang Mane, Kai Ye, Tian Zou, Wenbin Lin, Yunlong Xing, Zhaohu Bai, Jinbin Chai, Wenhao Zhu, Lei Maheshwari, Ritik Verma, Rakshank Tekchandanil, Rahul Hambarde, Praful Tazil, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Gao, Fan Yan, Weidan Shao, Wenze Zhang, Dengyin Chen, Bin Zhang, Siqi Qian, Yanxin Chen, Yuanbin Zhou, Yuanbo Tong, Tong Wei, Rongfeng Sun, Ruiqi Liu, Yue Akalwadi, Nikhil Joshi, Amogh Malagi, Sampada Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Dukre, Adinath Deshmukh, Vivek Phutke, Shruti S. Kulkarni, Ashutosh Gonde, Anil Karthik, Arun K. Manasa, N. Priyal, Shri Hari Hao, Wei Yan, Xingzhuo Fu, Minghan Univ Wurzburg Comp Vis Lab IFI & CAIDAS Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Prince Muqrin Univ Artificial Intelligence Dept Medinah 41311 Saudi Arabia Toyota Technol Inst Intelligent Informat Media Lab Nagoya Japan Nanjing Artificial Intelligence Res IA AiRiA Nanjing Peoples R China Nanjing Normal Univ High Sch Jiangning Campus Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Xiamen Univ Xiamen Peoples R China Natl Univ Singapore Singapore Singapore Univ Washington Seattle WA 98195 USA GEC Ajmer Kiranipura India CVPR Lab IIT Ropar Rupnagar India SCSS Trinity Coll Dublin Dublin Ireland Opt AI Seoul South Korea Nanjing Univ Posts & Telecommun Nanjing Peoples R China Fuzhou Univ Fuzhou Peoples R China Univ Hong Kong Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Univ Teknol Malaysia Malaysia Japan Int Inst Technol MMT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Shri Guru Gobind Singhji Inst Engn & Technol Nanded India Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Shiv Nadar Univ Sch Engn Chennai Tamil Nadu India Fortinet Inc Sunnyvale CA USA Bosch Investment Ltd Shanghai Peoples R China Univ Saskatchewan Saskatoon

ISBN: (纸本)9798350365474

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： lighting removal restoration shadow

来源：评论

学校读者我要写书评

暂无评论

Facial Expression Classification using Fusion of Deep Neural Network in Video

Facial Expression Classification using Fusion of Deep Neural...

引用

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

作者： Kim Ngan Phan Hong-Hai Nguyen Van-Thong Huynh Kim, Soo-Hyung Chonnam Natl Univ Dept Artificial Intelligence Convergence Gwangju South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35% and 28.60% for the F-1 score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset and our team archives 5th for the expression classification task in the 3rd Affective Behavior Analysis In-The-Wild competition.

关键词： Human computer interaction Emotion recognition Computational modeling Neural networks Transformers Behavioral sciences pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

Bootstrapping Chest CT Image Understanding by Distilling Kno...

引用

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

作者： Cao, Weiwei Zhang, Jianpeng Xia, Yingda Mok, Tony C. W. Li, Zi Ye, Xianghua Lu, Le Zheng, Jian Tang, Yuxing Zhang, Ling Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Beijing Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs, we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically, we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image, and perform pair-wise and semantic relation knowledge distillation. Subsequently, we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However, the challenge arises when patients have similar semantic diagnoses, such as healthy patients, potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios, including zero-shot learning, report generation, and fine-tuning processes, demonstrate the model's feasibility in interpreting chest CT images.

关键词： Medical vision language pre-training Report generation

来源：评论

学校读者我要写书评

暂无评论

Using Pure Pollen Species When Training a CNN to Segment Pollen Mixtures

Using Pure Pollen Species When Training a CNN to Segment Pol...

引用

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

作者： Yang, Nana Joos, Victor Jacquemart, A. L. Buyens, Christel De Vleeschouwer, C. UCLouvain ICTEAM Inst Ottignies Belgium UCLouvain ELI Inst Ottignies Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recognizing the types of pollen grains and estimating their proportion in pollen mixture samples collected in a specific geographical area is important for agricultural, medical, and ecosystem research. Our paper adopts a convolutional neural network for the automatic segmentation of pollen species in microscopy images, and proposes an original strategy to train such network at reasonable manual annotation cost. Our approach is founded on a large dataset composed of pure pollen images. It first (semi-)manually segments foreground, i.e. pollen grains, and background in a fraction of those images, and use the resulting annotated dataset to train a universal pollen segmentation CNN. In the second step, this model is used to automatically segment a large number of additional pure pollen images, so as to supervise the training of a pollen species segmentation model. Despite the fact that it has been trained from pure images only, the model is shown to provide accurate segmentation of species in pollen mixtures. Our experiments also demonstrate that dedicating a model to the segmentation of a subset of the available pure pollen species makes it possible to train a bin pollen class, corresponding to pollen species that are not in the subset of species recognized by the model. This strategy is useful to cope with unexpected species in a mixture.

关键词： Training Image segmentation computer vision Costs Computational modeling Microscopy pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

引用

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

作者： Jain, Himalaya Tuan-Hung Vu Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9781665487399

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains;ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2021 ieee/CVF conference on computer vision and pattern recognition, CVPR 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

引用

2021 ieee/CVF conference on computer vision and pattern recognition, CVPR 2021

ISBN: (纸本)9781665445092

The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old with the new: learning relative camera pose with geometry-guided uncertainty;uncertainty guided collaborative training for weakly supervised temporal action detection;privacy-preserving collaborative learning with automatic transformation search;rethinking and improving the robustness of image style transfer;style-aware normalized loss for improving arbitrary style transfer;faster meta update strategy for noise-robust deep learning;a hyperbolic-to-hyperbolic graph convolutional network;training networks in null space of feature covariance for continual learning;and exponential moving average normalization for self-supervised and semi-supervised learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Watch and Act: Dual Interacting Agents for Automatic Generat...

引用

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

作者： Sarkar, Saikat Mukherjee, Dipti Prasad Chakrabarti, Amlan Univ Calcutta Kolkata India Indian Stat Inst Kolkata India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.

关键词： Location awareness computer vision conferences Reinforcement learning Games computer architecture Real-time systems

来源：评论

学校读者我要写书评

暂无评论

ConvMLP: Hierarchical Convolutional MLPs for vision

ConvMLP: Hierarchical Convolutional MLPs for Vision

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Jiachen Hassani, Ali Walton, Steven Shi, Humphrey SHI Lab University of Oregon UIUC United States

ISBN: (纸本)9798350302493

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods on image classification. However, most methods adopt spatial MLPs which take fixed-dimension inputs, therefore making it difficult to apply them as backbones to downstream tasks such as object detection and semantic segmentation, which require inputs with arbitrary dimension. Moreover, single-stage designs further limit the performance in other computer vision tasks and fully-connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a Hierarchical Convolutional MLP for visual recognition, which is a lightweight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4 GMACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters. Our code and pre-trained models are open-sourced at https://***/SHI-Labs/Convolutional-MLPs. © 2023 ieee.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Categorized Reflection Removal Dataset with Diverse Real-world Scenes

A Categorized Reflection Removal Dataset with Diverse Real-w...

引用

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

作者： Lei, Chenyang Huang, Xuhua Qi, Chenyang Zhao, Yankun Sun, Wenxiu Yan, Qiong Chen, Qifeng HKUST Hong Kong Peoples R China CMU Pittsburgh PA USA SenseTime Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better.

关键词： computer vision conferences Pipelines Glass Benchmark testing Reflection 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 << < 74 75 76 77 78 79 80 81 82 83 > >>

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：