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

383 篇 会议

馆藏范围

383 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

383 篇 工学
- 383 篇 计算机科学与技术...
2 篇 医学
- 2 篇 临床医学

主题

383 篇 recognition: det...
382 篇 categorization
380 篇 retrieval
200 篇 deep learning
81 篇 representation l...
38 篇 vision applicati...
35 篇 segmentation
35 篇 scene analysis a...
27 篇 grouping and sha...
24 篇 gesture
24 篇 face
23 篇 and body pose
23 篇 datasets and eva...
20 篇 3d from multivie...
19 篇 video analytics
17 篇 statistical lear...
17 篇 vision + languag...
16 篇 optimization met...
13 篇 robotics + drivi...
13 篇 others

机构

16 篇 univ chinese aca...
15 篇 chinese univ hon...
11 篇 sun yat sen univ...
11 篇 sensetime res pe...
11 篇 peng cheng lab p...
9 篇 huawei noahs ark...
9 篇 carnegie mellon ...
8 篇 univ sci & techn...
8 篇 tsinghua univ pe...
8 篇 johns hopkins un...
7 篇 facebook ai res ...
7 篇 sun yat sen univ...
7 篇 minist educ key ...
7 篇 nanyang technol ...
6 篇 cas ctr excellen...
6 篇 hong kong univ s...
6 篇 tencent youtu la...
6 篇 tech univ munich...
6 篇 univ calif berke...
6 篇 univ texas austi...

作者

7 篇 zheng wei-shi
7 篇 wang xiaogang
7 篇 lin dahua
6 篇 ji rongrong
6 篇 yan junjie
5 篇 grauman kristen
5 篇 lin liang
5 篇 doermann david
5 篇 shao ling
5 篇 liang xiaodan
4 篇 shan shiguang
4 篇 zhang baochang
4 篇 girshick ross
4 篇 loy chen change
4 篇 chen xilin
4 篇 rastegari mohamm...
4 篇 tian qi
4 篇 lu cewu
4 篇 ouyang wanli
4 篇 schiele bernt

语言

383 篇 英文

检索条件"主题词=Recognition: Detection"

共 383 条记录，以下是91-100 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：

Re-Identification with Consistent Attentive Siamese Networks 32

Re-Identification with Consistent Attentive Siamese Networks

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Zheng, Meng Karanam, Srikrishna Wu, Ziyan Radke, Richard J. Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA Siemens Corp Technol Princeton NJ USA

ISBN: (纸本)9781728132938

We propose a new deep architecture for person re-identification (re-id). While re-id has seen much recent progress, spatial localization and view-invariant representation learning for robust cross-view matching remain key, unsolved problems. We address these questions by means of a new attention-driven Siamese learning architecture, called the Consistent Attentive Siamese Network. Our key innovations compared to existing, competing methods include (a) a flexible framework design that produces attention with only identity labels as supervision, (b) explicit mechanisms to enforce attention consistency among images of the same person, and (c) a new Siamese framework that integrates attention and attention consistency, producing principled supervisory signals as well as the first mechanism that can explain the reasoning behind the Siamese framework's predictions. We conduct extensive evaluations on the CUHK03-NP, DukeMTMC-ReID, and Market-1501 datasets and report competitive performance.

关键词： Categorization recognition: detection Retrieval Video Analytics

来源：评论

学校读者我要写书评

暂无评论

Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs 32

Wide-Area Crowd Counting via Ground-Plane Density Maps and M...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Zhang, Qi Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera, too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd. Therefore, to solve the wide-area counting task requires multiple cameras with overlapping fields-of-view. In this paper, we propose a deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene-level density map on the ground-plane of the 3D world. We consider 3 versions of the fusion framework: the late fusion model fuses camera-view density map;the naive early fusion model fuses camera-view feature maps;and the multi-view multi-scale early fusion model favors that features aligned to the same ground-plane point have consistent scales. We test our 3 fusion models on 3 multi-view counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view counting dataset containing a crowded street intersection. Our methods achieve state-of-the-art results compared to other multi-view counting baselines.

关键词： Categorization Grouping and S Motion and Tracking recognition: detection Retrieval Scene Analysis and Understanding Segmentation

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Relationship Inference for Grounding Referring Expressions 32

Cross-Modal Relationship Inference for Grounding Referring E...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Yang, Sibei Li, Guanbin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat sen Univ Guangzhou Guangdong Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Grounding referring expressions is a fundamental yet challenging task facilitating human-machine communication in the physical world. It locates the target object in an image on the basis of the comprehension of the relationships between referring natural language expressions and the image. A feasible solution for grounding referring expressions not only needs to extract all the necessary information (i.e. objects and the relationships among them) in both the image and referring expressions, but also compute and represent multimodal contexts from the extracted information. Unfortunately, existing work on grounding referring expressions cannot extract multi-order relationships from the referring expressions accurately and the contexts they obtain have discrepancies with the contexts described by referring expressions. In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships, that have connections with a given expression, with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph. In addition, we propose a Gated Graph Convolutional Network (GGCN) to compute multimodal semantic contexts by fusing information from different modes and propagating multimodal information in the structured relation graph. Experiments on various common benchmark datasets show that our Cross-Modal Relationship Inference Network, which consists of CMRE and GGCN, outperforms all existing state-of-the-art methods.

关键词： Categorization recognition: detection Retrieval Vision + Language

来源：评论

学校读者我要写书评

暂无评论

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning 32

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learnin...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Xin Yu, Fisher Wang, Ruth Darrell, Trevor Gonzalez, Joseph E. Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781728132938

Learning good feature embeddings for images often requires substantial training data. As a consequence, in settings where training data is limited (e.g., few-shot and zero-shot learning), we are typically forced to use a generic feature embedding across various tasks. Ideally, we want to construct feature embeddings that are tuned for the given task. In this work, we propose Task-Aware Feature Embedding Networks (TAFE-Nets(1)) to learn how to adapt the image representation to a new task in a meta learning fashion. Our network is composed of a meta learner and a prediction network. Based on a task input, the meta learner generates parameters for the feature layers in the prediction network so that the feature embedding can be accurately adjusted for that task. We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning. Our model matches or exceeds the state-of-the-art on all tasks. In particular, our approach improves the prediction accuracy of unseen attribute-object pairs by 4 to 15 points on the challenging visual attribute-object composition task.

关键词： Categorization recognition: detection Representation Learning Retrieval

来源：评论

学校读者我要写书评

暂无评论

Barrage of Random Transforms for Adversarially Robust Defense 32

Barrage of Random Transforms for Adversarially Robust Defens...

引用

IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Raff, Edward Sylvester, Jared Forsyth, Steven McLean, Mark Lab Phys Sci Columbia MD USA Booz Allen Hamilton Mclean VA 22102 USA NVIDIA Santa Clara CA USA UMBC Baltimore MD 21250 USA

ISBN: (纸本)9781728132938

Defenses against adversarial examples, when using the ImageNet dataset, are historically easy to defeat. The common understanding is that a combination of simple image transformations and other various defenses are insufficient to provide the necessary protection when the obfuscated gradient is taken into account. In this paper, we explore the idea of stochastically combining a large number of individually weak defenses into a single barrage of randomized transformations to build a strong defense against adversarial attacks. We show that, even after accounting for obfuscated gradients, the Barrage of Random Transforms (BaRT) is a resilient defense against even the most difficult attacks, such as PGD. BaRT achieves up to a 24x improvement in accuracy compared to previous work, and has even extended effectiveness out to a previously untested maximum adversarial perturbation of epsilon = 32.

关键词： Big Data Categorization Deep Learning Large Scale Methods recognition: detection Retrieval

来源：评论

学校读者我要写书评

暂无评论

Ranked List Loss for Deep Metric Learning 32

Ranked List Loss for Deep Metric Learning

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wang, Xinshao Hua, Yang Kodirov, Elyor Hu, Guosheng Garnier, Romain Robertson, Neil M. Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Anyvis Res Team London England

ISBN: (纸本)9781728132938

The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity information among data points. Existing pairwise or tripletwise loss functions used in DML are known to suffer from slow convergence due to a large proportion of trivial pairs or triplets as the model improves. To improve this, ranking-motivated structured losses are proposed recently to incorporate multiple examples and exploit the structured information among them. They converge faster and achieve state-of-the-art performance. In this work, we present two limitations of existing ranking-motivated structured losses and propose a novel ranked list loss to solve both of them. First, given a query, only a fraction of data points is incorporated to build the similarity structure. Consequently, some useful examples are ignored and the structure is less informative. To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery. The samples are split into a positive set and a negative set. Our objective is to make the query closer to the positive set than to the negative set by a margin. Second, previous methods aim to pull positive pairs as close as possible in the embedding space. As a result, the intraclass data distribution might be dropped. In contrast,we propose to learn a hypersphere for each class in order to preserve the similarity structure inside it. Our extensive experiments show that the proposed method achieves state-of-the-art performance on three widely used benchmarks.

关键词： Categorization Deep Learning recognition: detection Representation Learning Retrieval

来源：评论

学校读者我要写书评

暂无评论

Sequence-to-Sequence Domain Adaptation Network for Robust Text Image recognition 32

Sequence-to-Sequence Domain Adaptation Network for Robust Te...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Zhang, Yaping Nie, Shuai Liu, Wenju Xu, Xing Zhang, Dongxiang Shen, Heng Tao Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Afanti AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Domain adaptation has shown promising advances for alleviating domain shift problem. However, recent visual domain adaptation works usually focus on non-sequential object recognition with a global coarse alignment, which is inadequate to transfer effective knowledge for sequence-like text images with variable-length fine-grained character information. In this paper, we develop a Sequence-to-Sequence Domain Adaptation Network (SSDAN) for robust text image recognition, which could exploit unsupervised sequence data by an attention-based sequence encoder-decoder network. In the SSDAN, a gated attention similarity (GAS) unit is introduced to adaptively focus on aligning the distribution of the source and target sequence data in an attended character-level feature space rather than a global coarse alignment. Extensive text recognition experiments show the SSDAN could efficiently transfer sequence knowledge and validate the promising power of the proposed model towards real world applications in various recognition scenarios, including the natural scene text, handwritten text and even mathematical expression recognition.

关键词： Categorization Deep Learning Document Analysis recognition: detection Retrieval

来源：评论

学校读者我要写书评

暂无评论

Large-Scale Long-Tailed recognition in an Open World 32

Large-Scale Long-Tailed Recognition in an Open World

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Liu, Ziwei Miao, Zhongqi Zhan, Xiaohang Wang, Jiayun Gong, Boqing Yu, Stella X. Chinese Univ Hong Kong Hong Kong Peoples R China Univ Calif Berkeley ICSI Berkeley CA 94704 USA Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781728132938

Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance. We define Open Long-Tailed recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes. OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum. The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes. We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes. On three large-scale OLTR datasets we curate from object-centric ImageNet, scene-centric Places, and face-centric MS1M data, our method consistently outperforms the state-of-the-art.

关键词： Categorization recognition: detection Representation Learning Retrieval

来源：评论

学校读者我要写书评

暂无评论

Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks 32

Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Gr...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Reddy, N. Dinesh Vo, Minh Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728132938

We present Occlusion-Net(1), a framework to predict 2D and 3D locations of occluded keypoints for objects, in a largely self-supervised manner. We use an off-the-shelf detector as input (e.g. MaskRCNN [16]) that is trained only on visible key point annotations. This is the only supervision used in this work. A graph encoder network then explicitly classifies invisible edges and a graph decoder network corrects the occluded keypoint locations from the initial detector. Central to this work is a trifocal tensor loss that provides indirect self-supervision for occluded keypoint locations that are visible in other views of the object. The 2D keypoints are then passed into a 3D graph network that estimates the 3D shape and camera pose using the self-supervised reprojection loss. At test time, Occlusion-Net successfully localizes keypoints in a single view under a diverse set of occlusion settings. We validate our approach on synthetic CAD data as well as a large image set capturing vehicles at many busy city intersections. As an interesting aside, we compare the accuracy of human labels of invisible keypoints against those predicted by the trifocal tensor

关键词： 3D from Multiview and Sensors 3D from Single Image Categorization recognition: detection Retrieval Vision + Graphics

来源：评论

学校读者我要写书评

暂无评论

Transferrable Prototypical Networks for Unsupervised Domain Adaptation 32

Transferrable Prototypical Networks for Unsupervised Domain ...

引用

32nd IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Pan, Yingwei Yao, Ting Li, Yehao Wang, Yu Ngo, Chong-Wah Mei, Tao JD AI Res Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China City Univ Hong Kong Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar. Technically, TPN initially matches each target example to the nearest prototype in the source domain and assigns an example a "pseudo" label. The prototype of each class could then be computed on source-only, target-only and source-target data, respectively. The optimization of TPN is end-to-end trained by jointly minimizing the distance across the prototypes on three types of data and KL-divergence of score distributions output by each pair of the prototypes. Extensive experiments are conducted on the transfers across MNIST, USPS and SVHN datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain an accuracy of 80.4% of single model on VisDA 2017 dataset.

关键词： Categorization recognition: detection Retrieval

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39页 << < 6 7 8 9 10 11 12 13 14 15 > >>

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：