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

20,994 篇 会议
99 册 图书
86 篇 期刊文献
1 篇 学位论文

馆藏范围

21,179 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,604 篇 工学
- 11,179 篇 计算机科学与技术...
- 2,631 篇 机械工程
- 2,542 篇 软件工程
- 990 篇 光学工程
- 850 篇 电气工程
- 676 篇 控制科学与工程
- 487 篇 信息与通信工程
- 242 篇 仪器科学与技术
- 215 篇 测绘科学与技术
- 159 篇 生物医学工程（可授...
- 150 篇 生物工程
- 139 篇 电子科学与技术（可...
- 69 篇 安全科学与工程
- 67 篇 化学工程与技术
- 55 篇 建筑学
- 53 篇 土木工程
- 43 篇 力学（可授工学、理...
- 41 篇 航空宇航科学与技...
3,462 篇 医学
- 3,452 篇 临床医学
- 41 篇 基础医学(可授医学...
2,483 篇 理学
- 1,247 篇 数学
- 1,213 篇 物理学
- 446 篇 统计学（可授理学、...
- 418 篇 生物学
- 269 篇 系统科学
- 67 篇 化学
424 篇 管理学
- 218 篇 管理科学与工程(可...
- 217 篇 图书情报与档案管...
- 43 篇 工商管理
144 篇 艺术学
- 142 篇 设计学（可授艺术学...
41 篇 法学
31 篇 农学
12 篇 经济学
10 篇 教育学
6 篇 文学
3 篇 军事学

主题

8,072 篇 computer vision
2,879 篇 pattern recognit...
2,859 篇 training
1,808 篇 computational mo...
1,718 篇 visualization
1,478 篇 cameras
1,382 篇 shape
1,375 篇 face recognition
1,364 篇 three-dimensiona...
1,343 篇 feature extracti...
1,269 篇 image segmentati...
1,156 篇 robustness
1,109 篇 semantics
982 篇 layout
978 篇 object detection
953 篇 computer archite...
952 篇 benchmark testin...
931 篇 codes
918 篇 object recogniti...
899 篇 computer science

机构

174 篇 univ sci & techn...
154 篇 carnegie mellon ...
149 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
104 篇 zhejiang univ pe...
98 篇 swiss fed inst t...
93 篇 tsinghua univ pe...
92 篇 tsinghua univers...
90 篇 microsoft res as...
88 篇 shanghai ai lab ...
83 篇 zhejiang univers...
76 篇 alibaba grp peop...
74 篇 hong kong univ s...
73 篇 university of sc...
72 篇 peking univ peop...
68 篇 shanghai jiao to...
68 篇 university of ch...
66 篇 google res mount...
66 篇 univ oxford oxfo...

作者

83 篇 van gool luc
71 篇 zhang lei
60 篇 timofte radu
49 篇 yang yi
49 篇 luc van gool
48 篇 xiaoou tang
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
41 篇 qi tian
37 篇 vasconcelos nuno
37 篇 liu yang
37 篇 chen xilin
37 篇 li fei-fei
36 篇 liu xiaoming
36 篇 shan shiguang
36 篇 li stan z.
36 篇 torralba antonio
33 篇 zhou jie

语言

21,134 篇 英文
31 篇 中文
8 篇 其他
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"

共 21180 条记录，以下是1181-1190 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：

SIEDOB: Semantic Image Editing by Disentangling Object and Background

SIEDOB: Semantic Image Editing by Disentangling Object and B...

引用

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

作者： Luo, Wuyang Yang, Su Zhang, Xinjian Zhang, Weishan Fudan Univ Shanghai Key Lab Intelligent Informat Proc Sch Comp Sci Shanghai Peoples R China China Univ Petr Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9798350301298

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic Image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://***/WuyangLuo/SIEDOB.

关键词： vision applications and systems

来源：评论

学校读者我要写书评

暂无评论

Micron-BERT: BERT-based Facial Micro-Expression recognition

Micron-BERT: BERT-based Facial Micro-Expression Recognition

引用

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

作者： Nguyen, Xuan-Bac Duong, Chi Nhan Li, Xin Gauch, Susan Seo, Han-Seok Luu, Khoa Univ Arkansas CVIU Lab Little Rock AR 72204 USA Concordia Univ Montreal PQ Canada West Virginia Univ Morgantown WV USA Univ Arkansas Dep Food Sci Little Rock AR USA

ISBN: (纸本)9798350301298

Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-supervised learning tasks in computer vision. However, the standard BERT in vision problems is designed to learn only from full images or videos, and the architecture cannot accurately detect details of facial micro-expressions. This paper presents Micron-BERT (mu-BERT), a novel approach to facial micro-expression recognition. The proposed method can automatically capture these movements in an unsupervised manner based on two key ideas. First, we employ Diagonal Micro-Attention (DMA) to detect tiny differences between two frames. Second, we introduce a new Patch of Interest (PoI) module to localize and highlight micro-expression interest regions and simultaneously reduce noisy backgrounds and distractions. By incorporating these components into an end-to-end deep network, the proposed mu-BERT significantly outperforms all previous work in various micro-expression tasks. mu-BERT can be trained on a large-scale unlabeled dataset, i.e., up to 8 million images, and achieves high accuracy on new unseen facial micro-expression datasets. Empirical experiments show mu-BERT consistently outperforms state-of-the-art performance on four micro-expression benchmarks, including SAMM, CASME II, SMIC, and CASME3, by significant margins. Code will be available at https://***/uark-cviu/Micron-BERT

关键词： body gesture Humans: Face movement pose

来源：评论

学校读者我要写书评

暂无评论

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

引用

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

作者： Gao, Yipeng Lin, Kun-Yu Yan, Junkai Wang, Yaowei Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pengcheng Lab Shenzhen Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://***/Hlings/AsyFOD.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

ViTKD: Feature-based Knowledge Distillation for vision Transformers

ViTKD: Feature-based Knowledge Distillation for Vision Trans...

引用

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

作者： Yang, Zhendong Li, Zhe Zeng, Ailing Li, Zexian Yu, Chun Yu, Liming Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Beihang Univ Beijing Peoples R China IDEA Shenzhen Peoples R China

ISBN: (纸本)9798350365474

Knowledge Distillation (KD) has been extensively studied as a means to enhance the performance of smaller models in Convolutional Neural Networks (CNNs). Recently, the vision Transformer (ViT) has demonstrated remarkable success in various computer vision tasks, leading to an increased demand for KD in ViT. However, while logit-based KD has been applied to ViT, other feature-based KD methods for CNNs cannot be directly implemented due to the significant structure gap. In this paper, we conduct an analysis of the properties of different feature layers in ViT to identify a method for feature-based ViT distillation. Our findings reveal that both shallow and deep layers in ViT are equally important for distillation and require distinct distillation strategies. Based on these guidelines, we propose our feature-based method ViTKD, which mimics the shallow layers and generates the deep layer in the teacher. ViTKD leads to consistent and significant improvements in the students. On ImageNet-1K, we achieve performance boosts of 1.64% for DeiT-Tiny, 1.40% for DeiT-Small, and 1.70% for DeiT-Base. Downstream tasks also demonstrate the superiority of ViTKD. Additionally, ViTKD and logit-based KD are complementary and can be applied together directly, further enhancing the student's performance. Specifically, DeiT-T, S, and B achieve accuracies of 77.78%, 83.59%, and 85.41%, respectively, using this combined approach. Code is available at https:// github. com/ yzdv/cls_KD.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

PaCa-ViT: Learning Patch-to-Cluster Attention in vision Transformers

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Tran...

引用

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

作者： Grainger, Ryan Paniagua, Thomas Song, Xi Cuntoor, Naresh Lee, Mun Wai Wu, Tianfu NC State Dept ECE Raleigh NC 27695 USA BlueHalo Arlington VA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) are built on the assumption of treating image patches as "visual tokens" and learn patch-to-patch attention. The patch embedding based tokenizer has a semantic gap with respect to its counterpart, the textual tokenizer. The patch-to-patch attention suffers from the quadratic complexity issue, and also makes it nontrivial to explain learned ViTs. To address these issues in ViT, this paper proposes to learn Patch-to-Cluster attention (PaCa) in ViT. Queries in our PaCa-ViT starts with patches, while keys and values are directly based on clustering (with a predefined small number of clusters). The clusters are learned end-to-end, leading to better tokenizers and inducing joint clustering-for-attention and attention-for-clustering for better and interpretable models. The quadratic complexity is relaxed to linear complexity. The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks. In experiments, the proposed methods are tested on ImageNet-1k image classification, MS-COCO object detection and instance segmentation and MIT-ADE20k semantic segmentation. Compared with the prior art, it obtains better performance in all the three benchmarks than the SWin [32] and the PVTs [47, 48] by significant margins in ImageNet-1k and MIT-ADE20k. It is also significantly more efficient than PVT models in MS-COCO and MIT-ADE20k due to the linear complexity. The learned clusters are semantically meaningful. Code and model checkpoints are available at https://***/iVMCL/PaCaViT.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

Discriminating Known from Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder

Discriminating Known from Unknown Objects via Structure-Enha...

引用

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

作者： Wu, Aming Deng, Cheng Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350301298

Discriminating known from unknown objects is an important essential ability for human beings. To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors. Due to lacking unknown data for supervision, for this task, the main challenge lies in how to leverage the known in-distribution (ID) data to improve the detector's discrimination ability. In this paper, we first propose a method of Structure-Enhanced Recurrent Variational AutoEncoder (SR-VAE), which mainly consists of two dedicated recurrent VAE branches. Specifically, to boost the performance of object localization, we explore utilizing the classical Laplacian of Gaussian (LoG) operator to enhance the structure information in the extracted low-level features. Meanwhile, we design a VAE branch that recurrently generates the augmentation of the classification features to strengthen the discrimination ability of the object classifier. Finally, to alleviate the impact of lacking unknown data, another cycle-consistent conditional VAE branch is proposed to synthesize virtual OOD features that deviate from the distribution of ID features, which improves the capability of distinguishing OOD objects. In the experiments, our method is evaluated on OOD-OD, open-vocabulary detection, and incremental object detection. The significant performance gains over baselines show the superiorities of our method. The code will be released at https://***/AmingWu/SR-VAE.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustne...

引用

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

作者： Li, Zhuowan Wang, Xingrui Stengel-Eskin, Elias Kortylewski, Adam Ma, Wufei Van Durme, Benjamin Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Univ Southern Calif Los Angeles CA USA Max Planck Inst Informat Saarbrucken Germany Univ Freiburg Freiburg Germany

ISBN: (纸本)9798350301298

Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL [45] and NSVQA [59], and two non-symbolic methods FiLM [50] and mDETR [29];and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts. The dataset and code are released at https://***/Lizw14/Super-CLEVR.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

Masked Jigsaw Puzzle: A Versatile Position Embedding for vision Transformers

Masked Jigsaw Puzzle: A Versatile Position Embedding for Vis...

引用

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

作者： Ren, Bin Liu, Yahui Song, Yue Bi, Wei Cucchiara, Rita Sebe, Nicu Wang, Wei Univ Pisa Pisa Italy Univ Trento Trento TN Italy Tencent AI Lab Shenzhen Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350301298

Position Embeddings (PEs), an arguably indispensable component in vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack;2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy;3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://***/yhlleo/ MJP.

关键词： Deep learning architectures and techniques

来源：评论

学校读者我要写书评

暂无评论

Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection

Object-Aware Distillation Pyramid for Open-Vocabulary Object...

引用

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

作者： Wang, Luting Liu, Yi Du, Penghui Ding, Zihan Liao, Yue Qi, Qiaosong Chen, Biaolong Liu, Si Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary object detection aims to provide object detectors trained on a fixed set of object categories with the generalizability to detect objects described by arbitrary text queries. Previous methods adopt knowledge distillation to extract knowledge from Pretrained vision-and-Language Models (PVLMs) and transfer it to detectors. However, due to the non-adaptive proposal cropping and single-level feature mimicking processes, they suffer from information destruction during knowledge extraction and inefficient knowledge transfer. To remedy these limitations, we propose an Object-Aware Distillation Pyramid (OADP) framework, including an Object-Aware Knowledge Extraction (OAKE) module and a Distillation Pyramid (DP) mechanism. When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects. The latter introduces global and block distillation for more comprehensive knowledge transfer to compensate for the missing relation information in object distillation. Extensive experiments show that our method achieves significant improvement compared to current methods. Especially on the MS-COCO dataset, our OADP framework reaches 35.6 mAP(50)(N), surpassing the current state-of-the-art method by 3.3 mAP(50)(N). Code is released at https://***/LutingWang/OADP.

关键词： detection recognition: Categorization retrieval

来源：评论

学校读者我要写书评

暂无评论

Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images

Sparse Multi-Modal Graph Transformer with Shared-Context Pro...

引用

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

作者： Nakhli, Ramin Moghadam, Puria Azadi Mi, Haoyang Farahani, Hossein Baras, Alexander Gilks, Blake Bashashati, Ali Univ British Columbia Vancouver BC Canada Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350301298

Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore explicit information about the individual cells within a patch. In this paper, by defining the novel concept of shared-context processing, we designed a multi-modal Graph Transformer (AMIGO) that uses the cellular graph within the tissue to provide a single representation for a patient while taking advantage of the hierarchical structure of the tissue, enabling a dynamic focus between cell-level and tissue-level information. We benchmarked the performance of our model against multiple state-of-the-art methods in survival prediction and showed that ours can significantly outperform all of them including hierarchical vision Transformer (ViT). More importantly, we show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data. Finally, in two different cancer datasets, we demonstrated that our model was able to stratify the patients into low-risk and high-risk groups while other state-of-the-art methods failed to achieve this goal. We also publish a large dataset of immunohistochemistry images (InUIT) containing 1,600 tissue microarray (TMA) cores from 188 patients along with their survival information, making it one of the largest publicly available datasets in this context.

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

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 << < 115 116 117 118 119 120 121 122 123 124 > >>

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：