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

20,860 篇 会议
105 篇 期刊文献
43 册 图书

馆藏范围

21,007 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,620 篇 工学
- 11,056 篇 计算机科学与技术...
- 2,652 篇 机械工程
- 2,252 篇 软件工程
- 914 篇 光学工程
- 885 篇 电气工程
- 529 篇 控制科学与工程
- 477 篇 信息与通信工程
- 216 篇 测绘科学与技术
- 135 篇 生物工程
- 127 篇 生物医学工程（可授...
- 98 篇 电子科学与技术（可...
- 92 篇 仪器科学与技术
- 46 篇 安全科学与工程
- 40 篇 建筑学
- 40 篇 化学工程与技术
- 39 篇 土木工程
- 37 篇 交通运输工程
- 35 篇 力学（可授工学、理...
- 33 篇 航空宇航科学与技...
3,494 篇 医学
- 3,489 篇 临床医学
- 32 篇 基础医学(可授医学...
2,247 篇 理学
- 1,145 篇 物理学
- 1,081 篇 数学
- 401 篇 生物学
- 384 篇 统计学（可授理学、...
- 245 篇 系统科学
- 46 篇 化学
343 篇 管理学
- 176 篇 管理科学与工程(可...
- 168 篇 图书情报与档案管...
- 34 篇 工商管理
31 篇 法学
19 篇 农学
15 篇 教育学
8 篇 经济学
5 篇 艺术学
2 篇 军事学
1 篇 文学

主题

8,140 篇 computer vision
2,886 篇 training
2,838 篇 pattern recognit...
1,812 篇 computational mo...
1,707 篇 visualization
1,493 篇 cameras
1,431 篇 three-dimensiona...
1,428 篇 feature extracti...
1,360 篇 shape
1,352 篇 face recognition
1,255 篇 image segmentati...
1,135 篇 robustness
1,134 篇 semantics
988 篇 computer archite...
983 篇 object detection
974 篇 layout
966 篇 benchmark testin...
930 篇 codes
903 篇 object recogniti...
900 篇 computer science

机构

174 篇 univ sci & techn...
158 篇 univ chinese aca...
146 篇 carnegie mellon ...
145 篇 chinese univ hon...
109 篇 microsoft resear...
103 篇 zhejiang univ pe...
100 篇 swiss fed inst t...
95 篇 tsinghua univers...
90 篇 microsoft res as...
88 篇 shanghai ai lab ...
85 篇 tsinghua univ pe...
81 篇 zhejiang univers...
77 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
70 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
65 篇 google res mount...

作者

80 篇 van gool luc
70 篇 zhang lei
57 篇 timofte radu
51 篇 luc van gool
48 篇 yang yi
46 篇 xiaoou tang
44 篇 tian qi
43 篇 darrell trevor
43 篇 li stan z.
42 篇 loy chen change
42 篇 qi tian
41 篇 sun jian
39 篇 li fei-fei
38 篇 tao dacheng
37 篇 chen xilin
36 篇 shan shiguang
36 篇 torralba antonio
35 篇 zhou jie
35 篇 vasconcelos nuno
35 篇 liu yang

语言

20,977 篇 英文
12 篇 其他
10 篇 中文
5 篇 土耳其文
2 篇 日文
2 篇 葡萄牙文

检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1401-1410 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：

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework...

引用

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

作者： Shin, Hyungseob Kim, Hyeongyu Kim, Sewon Jun, Yohan Eo, Taejoon Hwang, Dosik Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Yonsei Univ Dept Oral & Maxillofacial Radiol Coll Dent Seoul South Korea Yonsei Univ Dept Radiol Seoul South Korea Yonsei Univ Ctr Clin Imaging Data Sci Coll Med Seoul South Korea Naver AI Lab Bundangdong South Korea Naver Cloud Seongnam South Korea Probe Med Inc Seoul South Korea Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA USA Korea Inst Sci & Technol Ctr Healthcare Robot Seoul South Korea

ISBN: (纸本)9798350301298

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra-and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

关键词： cell microscopy Medical and biological vision

来源：评论

学校读者我要写书评

暂无评论

ViLEM: Visual-Language Error Modeling for Image-Text Retrieval

ViLEM: Visual-Language Error Modeling for Image-Text Retriev...

引用

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

作者： Chen, Yuxin Zhang, Zongyang Zhang, Ziqi Qi, Zhongang Yuan, Chunfeng Shan, Ying Li, Bing Hu, Weiming Qie, Xiaohu Wu, JianPing Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing Peoples R China ARC Lab Singapore Singapore Tencent PCG Shenzhen Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Dominant pre-training works for image-text retrieval adopt "dual-encoder" architecture to enable high efficiency, where two encoders are used to extract image and text representations and contrastive learning is employed for global alignment. However, coarse-grained global alignment ignores detailed semantic associations between image and text. In this work, we propose a novel proxy task, named Visual-Language ErrorModeling (ViLEM), to inject detailed image-text association into "dual-encoder" model by "proofreading" each word in the text against the corresponding image. Specifically, we first edit the image-paired text to automatically generate diverse plausible negative texts with pre-trained language models. ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information. Furthermore, we propose a multi-granularity interaction framework to perform ViLEM via interacting text features with both global and local image features, which associates local text semantics with both high-level visual context and multi-level local visual information. Our method surpasses state-of-the-art "dual-encoder" methods by a large margin on the image-text retrieval task and significantly improves discriminativeness to local textual semantics. Our model can also generalize well to video-text retrieval.

关键词： and reasoning language vision

来源：评论

学校读者我要写书评

暂无评论

Long-Tailed Visual recognition via Self-Heterogeneous Integration with Knowledge Excavation

Long-Tailed Visual Recognition via Self-Heterogeneous Integr...

引用

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

作者： Jin, Yan Li, Mengke Lu, Yang Cheung, Yiu-ming Wang, Hanzi Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China Xiamen Univ Minist Educ China Key Lab Multimedia Trusted Percept & Efficient Co Xiamen Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution. Experts in these methods are with the same model depth, which neglects the fact that different classes may have different preferences to be fit by models with different depths. To this end, we propose a novel MoE-based method called Self-Heterogeneous Integration with Knowledge Excavation (SHIKE). We first propose Depth-wise Knowledge Fusion (DKF) to fuse features between different shallow parts and the deep part in one network for each expert, which makes experts more diverse in terms of representation. Based on DKF, we further propose Dynamic Knowledge Transfer (DKT) to reduce the influence of the hardest negative class that has a non-negligible impact on the tail classes in our MoE framework. As a result, the classification accuracy of long-tailed data can be significantly improved, especially for the tail classes. SHIKE achieves the state-of-the-art performance of 56.3%, 60.3%, 75.4% and 41.9% on CIFAR100-LT (IF100), ImageNet-LT, iNaturalist 2018, and Places-LT, respectively. The source code is available at https://***/jinyan-06/SHIKE.

关键词： continual low-shot meta or long-tail learning Transfer

来源：评论

学校读者我要写书评

暂无评论

Appearance Label Balanced Triplet Loss for Multi-modal Aerial View Object Classification

Appearance Label Balanced Triplet Loss for Multi-modal Aeria...

引用

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

作者： Puttagunta, Raghunath Sai Li, Zhu Bhattacharyya, Shuvra York, George Univ Missouri Kansas City MO 64110 USA Univ Maryland College Pk MD USA US Air Force Acad Colorado Springs CO USA

ISBN: (纸本)9798350302493

Automatic target recognition (ATR) using image data is an important computer vision task with widespread applications in remote sensing for surveillance, object tracking, urban planning, agriculture, and more. Although there have been continuous advancements in this task, there is still significant room for further advancements, particularly with aerial images. This work extracts rich information from multimodal synthetic aperture radar (SAR) and electro-optical (EO) aerial images to perform object classification. Compared to EO images, the advantages of SAR images are that they can be captured at night and in any weather condition. Compared to EO images, the disadvantage of SAR images is that they are noisy. Overcoming the noise inherent to SAR images is a challenging, but worthwhile, task because of the additional information SAR images provide the model. This work proposes a training strategy that involves the creation of appearance labels to generate triplet pairs for training the network with both triplet loss and cross-entropy loss. During the development phase of the 2023 Perception Beyond Visual Spectrum (PBVS) Multi-modal Aerial Image Object Classification (MAVOC) challenge, our ResNet-34 model achieved a top-1 accuracy of 64.29% for Track 1 and our ensemble learning model achieved a top-1 accuracy 75.84% for Track 2. These values are 542% and 247% higher than the baseline values. Overall, this work ranked 3rd in both Track 1 and Track 2.

关键词： Automatic target recognition

来源：评论

学校读者我要写书评

暂无评论

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

CABM: Content-Aware Bit Mapping for Single Image Super-Resol...

引用

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

作者： Tian, Senmao Lu, Ming Liu, Jiaming Guo, Yandong Chen, Yurong Zhang, Shunli Beijing Jiaotong Univ Beijing Peoples R China OPPO Res Inst Shenzhen Peoples R China Intel Labs China Beijing Peoples R China Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

LP-DIF: Learning Local pattern-specific Deep Implicit Function for 3D Objects and Scenes

LP-DIF: Learning Local Pattern-specific Deep Implicit Functi...

引用

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

作者： Wang, Meng Liu, Yu-Shen Gao, Yue Shi, Kanle Fang, Yi Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Beijing Peoples R China New York Univ Abu Dhabi Ctr Artificial Intelligence & Robot Abu Dhabi U Arab Emirates Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

Deep Implicit Function (DIF) has gained much popularity as an efficient 3D shape representation. To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder. Such local methods can capture more local details due to less diversity among local regions than global shapes. Although the diversity of local regions has been decreased compared to global approaches, the diversity in different local regions still poses a challenge in learning an implicit function when treating all regions equally using only a single decoder. What is worse, these local regions often exhibit imbalanced distributions, where certain regions have significantly fewer observations. This leads that fine geometry details could not be preserved well. To solve this problem, we propose a novel Local pattern-specific Implicit Function, named LP-DIF, to represent a shape with clusters of local regions and multiple decoders, where each decoder only focuses on one cluster of local regions which share a certain pattern. Specifically, we first extract local codes for all regions, and then cluster them into multiple groups in the latent space, where similar regions sharing a common pattern fall into one group. After that, we train multiple decoders for mining local patterns of different groups, which simplifies the learning of fine geometric details by reducing the diversity of local regions seen by each decoder. To further alleviate the data-imbalance problem, we introduce a region re-weighting module to each pattern-specific decoder using a kernel density estimator, which dynamically re-weights the regions during learning. Our LP-DIF can restore more geometry details, and thus improve the quality of 3D reconstruction. Experiments demonstrate that our method can achieve the state-of-the-art performance over previous methods. Code is available at https://***/gtyxyz/lpdif.

关键词： 3D from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Scene Graph Driven Text-Prompt Generation for Image Inpainting

Scene Graph Driven Text-Prompt Generation for Image Inpainti...

引用

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

作者： Shukla, Tripti Maheshwari, Paridhi Singh, Rajhans Shukla, Ankita Kulkarni, Kuldeep Turaga, Pavan Adobe Res India San Jose CA 95110 USA Stanford Univ Stanford CA USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798350302493

Scene editing methods are undergoing a revolution, driven by text-to-image synthesis methods. Applications in media content generation have benefited from a careful set of engineered text prompts, that have been arrived at by the artists by trial and error. There is a growing need to better model prompt generation, for it to be useful for a broad range of consumer-grade applications. We propose a novel method for text prompt generation for the explicit purpose of consumer-grade image inpainting, i.e. insertion of new objects into missing regions in an image. Our approach leverages existing inter-object relationships to generate plausible textual descriptions for the missing object, that can then be used with any text-to-image generator. Given an image and a location where a new object is to be inserted, our approach first converts the given image to an intermediate scene graph. Then, we use graph convolutional networks to 'expand' the scene graph by predicting the identity and relationships of the new object to be inserted, with respect to the existing objects in the scene. The output of the expanded scene graph is cast into a textual description, which is then processed by a text-to-image generator, conditioned on the given image, to produce the final inpainted image. We conduct extensive experiments on the Visual Genome dataset, and show through qualitative and quantitative metrics that our method is superior to other methods.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Training Strategies for vision Transformers for Object Detection

Training Strategies for Vision Transformers for Object Detec...

引用

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

作者： Singh, Apoorv Motional Boston MA 02210 USA

ISBN: (纸本)9798350302493

vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in Sec. 3. These strategies brings down vision Transformers detectors [3, 15, 18, 19, 36] inference-time even less than traditional single-image based CNN detectors like FCOS [17, 25, 33]. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Mitigating Catastrophic Interference using Unsupervised Multi-Part Attention for RGB-IR Face recognition

Mitigating Catastrophic Interference using Unsupervised Mult...

引用

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

作者： Nikhal, Kshitij Uzuegbunam, Nkiruka Kennedy, Bridget Riggan, Benjamin S. Univ Nebraska Lincoln NE 68583 USA BlueHalo Arlington VA USA

ISBN: (纸本)9798350302493

Modern algorithms for RGB-IR facial recognitiona challenging problem where infrared probe images are matched with visible gallery images-leverage precise and accurate guidance from curated (i.e., labeled) data to bridge large spectral differences. However, supervised cross-spectral face recognition methods are often extremely sensitive due to over-fitting to labels, performing well in some settings but not in others. Moreover, when finetuning on data from additional settings, supervised cross-spectral face recognition are prone to catastrophic forgetting. Therefore, we propose a novel unsupervised framework for RGB-IR face recognition to minimize the cost and time inefficiencies pertaining to labeling large-scale, multi-spectral data required to train supervised cross-spectral recognition methods and to alleviate the effect of forgetting by removing over dependence on hard labels to bridge such large spectral differences. The proposed framework integrates an efficient backbone network architecture with part-based attention models, which collectively enhances common information between visible and infrared faces. Then, the framework is optimized using pseudo-labels and a new cross-spectral memory bank loss. This framework is evaluated on the ARL-VTF and TUFTS datasets, achieving 98.55% and 43.28% true accept rate, respectively. Additionally, we analyze effects of forgetting and show that our framework is less prone to these effects.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset

RaBit: Parametric Modeling of 3D Biped Cartoon Characters wi...

引用

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

作者： Luo, Zhongjin Cai, Shengcai Dong, Jinguo Ming, Ruibo Qiu, Liangdong Zhan, Xiaohang Han, Xiaoguang CUHKSZ SSE Shenzhen Peoples R China CUHKSZ FNii Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-based approaches have achieved unprecedented accuracy and efficiency in the area of 3D real human digitization. However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming. In this paper, we introduce 3DBiCar, the first large-scale dataset of 3D biped cartoon characters, and RaBit, the corresponding parametric model. Our dataset contains 1,500 topologically consistent high-quality 3D textured models which are manually crafted by professional artists. Built upon the data, RaBit is thus designed with a SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture generator, simultaneously expressing the shape, pose, and texture. To demonstrate the practicality of 3DBiCar and RaBit, various applications are conducted, including single-view reconstruction, sketch-based modeling, and 3D cartoon animation. For the single-view reconstruction setting, we find a straightforward global mapping from input images to the output UV-based texture maps tends to lose detailed appearances of some local parts (e.g., nose, ears). Thus, a part-sensitive texture reasoner is adopted to make all important local areas perceived. Experiments further demonstrate the effectiveness of our method both qualitatively and quantitatively. 3DBiCar and RaBit are available at ***/projects/RaBit.

关键词： vision + graphics

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 << < 137 138 139 140 141 142 143 144 145 146 > >>

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：