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

19,438 篇 会议
46 篇 期刊文献
5 册 图书

馆藏范围

19,488 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

12,440 篇 工学
- 10,282 篇 计算机科学与技术...
- 2,395 篇 机械工程
- 2,007 篇 软件工程
- 813 篇 光学工程
- 531 篇 电气工程
- 419 篇 控制科学与工程
- 322 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 80 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 70 篇 生物工程
- 60 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 土木工程
- 33 篇 力学（可授工学、理...
- 31 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 20 篇 材料科学与工程（可...
- 20 篇 交通运输工程
3,409 篇 医学
- 3,408 篇 临床医学
1,980 篇 理学
- 1,006 篇 数学
- 973 篇 物理学
- 359 篇 统计学（可授理学、...
- 336 篇 生物学
- 231 篇 系统科学
- 24 篇 化学
258 篇 管理学
- 138 篇 管理科学与工程(可...
- 122 篇 图书情报与档案管...
- 27 篇 工商管理
19 篇 法学
- 19 篇 社会学
14 篇 农学
8 篇 教育学
7 篇 经济学
3 篇 军事学
3 篇 艺术学

主题

7,893 篇 computer vision
2,727 篇 training
2,680 篇 pattern recognit...
1,760 篇 computational mo...
1,644 篇 visualization
1,410 篇 cameras
1,372 篇 three-dimensiona...
1,327 篇 shape
1,213 篇 face recognition
1,207 篇 image segmentati...
1,164 篇 feature extracti...
1,109 篇 robustness
1,087 篇 semantics
983 篇 layout
959 篇 object detection
949 篇 computer archite...
942 篇 benchmark testin...
931 篇 codes
902 篇 computer science
859 篇 deep learning

机构

174 篇 univ sci & techn...
161 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
106 篇 tsinghua univ pe...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
92 篇 tsinghua univers...
89 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
73 篇 university of sc...
73 篇 hong kong univ s...
72 篇 peking univ peop...
72 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
66 篇 shanghai jiao to...

作者

79 篇 van gool luc
70 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
45 篇 luc van gool
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
38 篇 li stan z.
36 篇 chen xilin
36 篇 torralba antonio
35 篇 vasconcelos nuno
35 篇 shan shiguang
35 篇 liu yang
34 篇 liu xiaoming
34 篇 tao dacheng

语言

19,484 篇 英文
2 篇 日文
2 篇 中文
1 篇 其他

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000"

共 19489 条记录，以下是4911-4920 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：

Transferring Cross-domain Knowledge for Video Sign Language recognition

Transferring Cross-domain Knowledge for Video Sign Language ...

引用

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

作者： Li, Dongxu Yu, Xin Xu, Chenchen Petersson, Lars Li, Hongdong Australian Natl Univ Canberra ACT Australia Australian Ctr Robot Vis ACRV Brisbane Qld Australia Univ Technol Sydney Sydney NSW Australia DATA61 CSIRO Sydney NSW Australia

ISBN: (纸本)9781728171685

Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of large domain gaps, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant descriptors and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domains. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for AP@0.5.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation 31

Weakly-supervised Deep Convolutional Neural Network Learning...

引用

31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yong Dong, Weiming Hu, Bao-Gang Ji, Qiang CASIA Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)9781538664209

Facial action unit (AU) intensity estimation plays an important role in affective computing and human-computer interaction. Recent works have introduced deep neural networks for AU intensity estimation, but they require a large amount of intensity annotations. AU annotation needs strong domain expertise and it is expensive to construct a large database to learn deep models. We propose a novel knowledge-based semi-supervised deep convolutional neural network for AU intensity estimation with extremely limited AU annotations. Only the intensity annotations of peak and valley frames in training sequences are needed. To provide additional supervision for model learning, we exploit naturally existing constraints on AUs, including relative appearance similarity, temporal intensity ordering, facial symmetry, and contrastive appearance difference. Experimental evaluations are performed on two public benchmark databases. With around 2% of intensity annotations in FERA 2015 and around 1% in DISFA for training, our method can achieve comparable or even better performance than the state-of-the-art methods which use 100% of intensity annotations in the training set.

关键词： Gold Estimation Training Face Image segmentation Neural networks

来源：评论

学校读者我要写书评

暂无评论

Adaptive Aggregation Networks for Class-Incremental Learning

Adaptive Aggregation Networks for Class-Incremental Learning

引用

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

作者： Liu, Yaoyao Schiele, Bernt Sun, Qianru Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9781665445092

Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets) in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances(1).

关键词： Adaptation models computer vision Adaptive systems computer architecture Network architecture Benchmark testing Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Prior Based Human Completion

Prior Based Human Completion

引用

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

作者： Zhao, Zibo Liu, Wen Xu, Yanyu Chen, Xianing Luo, Weixin Jin, Lei Zhu, Bohui Liu, Tong Zhao, Binqiang Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China ASTAR Inst High Performance Comp Singapore Singapore Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Taobao Hangzhou Peoples R China

ISBN: (纸本)9781665445092

We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of learned priors to compensate for the damaged region. One is a structure prior, it uses a human parsing map to represent the human body structure. The other is a structure-texture correlation prior. It learns a structure and a texture memory bank, which encodes the common body structures and texture patterns, respectively. With the aid of these memory banks, the model could utilize the visible pattern to query and fetch a similar structure and texture pattern to introduce additional reasonable structures and textures for the corrupted region. Besides, since multiple potential human shapes are underlying the corrupted region, we propose multi-scale structure discriminators to further restore a plausible topological structure. Experiments on various large-scale benchmarks demonstrate the effectiveness of our proposed method.

关键词： Image segmentation computer vision Correlation Shape Biological system modeling Computational modeling Semantics

来源：评论

学校读者我要写书评

暂无评论

Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression

Room-and-Object Aware Knowledge Reasoning for Remote Embodie...

引用

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

作者： Gao, Chen Chen, Jinyu Liu, Si Wang, Luting Zhang, Qiong Wu, Qi Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Univ Adelaide Adelaide SA Australia Xiaomi Inc Xiaomi AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction. Different from related VLN tasks, the key to REVERIE is to conduct goal-oriented exploration instead of strict instruction-following, due to the lack of step-by-step navigation guidance. In this paper, we propose a novel Cross-modality Knowledge Reasoning (CKR) model to address the unique challenges of this task. The CKR, based on a transformer-architecture, learns to generate scene memory tokens and utilise these informative history clues for exploration. Particularly, a Room-and-Object Aware Attention (ROAA) mechanism is devised to explicitly perceive the room- and object-type information from both linguistic and visual observations. Moreover, through incorporating commonsense knowledge, we propose a Knowledge-enabled Entity Relationship Reasoning (KERR) module to learn the internal-external correlations among room- and object-entities for agent to make proper action at each viewpoint. Evaluation on REVERIE benchmark demonstrates the superiority of the CKR model, which significantly boosts SPL and REVERIE-success rate by 64.67% and 46.05%, respectively. Code is available at: https://***/alloldman/CKR.

关键词： Visualization Correlation Navigation Linguistics Transformers Cognition pattern recognition

来源：评论

学校读者我要写书评

暂无评论

A regularized spectral algorithm for Hidden Markov Models with applications in computer vision

A regularized spectral algorithm for Hidden Markov Models wi...

引用

2012 ieee conference on computer vision and pattern recognition, cvpr 2012

作者： Minh, Ha Quang Cristani, Marco Perina, Alessandro Murino, Vittorio Genoa 16163 Italy Microsoft Research WA United States

ISBN: (纸本)9781467312264

Hidden Markov Models (HMMs) are among the most important and widely used techniques to deal with sequential or temporal data. Their application in computer vision ranges from action/gesture recognition to videosurveillance through shape analysis. Although HMMs are often embedded in complex frameworks, this paper focuses on theoretical aspects of HMM learning. We propose a regularized algorithm for learning HMMs in the spectral framework, whose computations have no local minima. Compared with recently proposed spectral algorithms for HMMs, our method is guaranteed to produce probability values which are always physically meaningful and which, on synthetic mathematical models, give very good approximations to true probability values. Furthermore, we place no restriction on the number of symbols and the number of states. On various pattern recognition data sets, our algorithm consistently outperforms classical HMMs, both in accuracy and computational speed. This and the fact that HMMs are used in vision as building blocks for more powerful classification approaches, such as generative embedding approaches or more complex generative models, strongly support spectral HMMs (SHMMs) as a new basic tool for pattern recognition. © 2012 ieee.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

SoftGroup for 3D Instance Segmentation on Point Clouds

SoftGroup for 3D Instance Segmentation on Point Clouds

引用

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

作者： Thang Vu Kim, Kookhoi Luu, Tung M. Thanh Nguyen Yoo, Chang D. Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 in terms of AP(50) . Soft-Group is also fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at https://***/thangvubk/***.

关键词： Point cloud compression Measurement computer vision Three-dimensional displays Codes Semantics pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning

Learning Bayesian Sparse Networks with Full Experience Repla...

引用

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

作者： Yan, Qingsen Gong, Dong Liu, Yuhang van den Hengel, Anton Shi, Javen Qinfeng Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks which leads to catastrophic forgetting. To ameliorate this problem, we propose to only activate and select sparse neurons for learning current and past tasks at any stage. More parameters space and model capacity can thus be reserved for the future tasks. This minimizes the interference between parameters for different tasks. To do so, we propose a Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers. Full Experience Replay (FER) provides effective supervision in learning the sparse activations of the neurons in different layers. A loss-aware reservoir-sampling strategy is developed to maintain the memory buffer. The proposed method is agnostic as to the network structures and the task boundaries. Experiments on different datasets show that SNCL achieves state-of-the-art result for mitigating forgetting.

关键词： computer vision Correlation Neurons Interference Machine learning Reservoirs Bayes methods

来源：评论

学校读者我要写书评

暂无评论

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis

Learning Neural Representation of Camera Pose with Matrix Re...

引用

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

作者： Zhu, Yaxuan Gao, Ruiqi Huang, Siyuan Zhu, Song-Chun Wu, Ying Nian Univ Calif Los Angeles UCLA Dept Stat Los Angeles CA 90095 USA

ISBN: (纸本)9781665445092

How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression.

关键词： computer vision Three-dimensional displays Shape Algebra Quaternions Cameras Matrices

来源：评论

学校读者我要写书评

暂无评论

Rank-One Prior: Toward Real-Time Scene Recovery

Rank-One Prior: Toward Real-Time Scene Recovery

引用

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

作者： Liu, Jun Liu, Ryan Wen Sun, Jianing Zeng, Tieyong Northeast Normal Univ Sch Math & Stat Changchun Peoples R China Northeast Normal Univ Key Lab Appl Stat MOE Changchun Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Northeast Normal Univ Jilin Natl Appl Math Ctr Changchun Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwater, and haze. The heart of our work is that we propose an intensity projection strategy to estimate the transmission. This strategy is motivated by a straightforward rank-one transmission prior. The complexity of transmission estimation is O(N) where N is the size of the single image. Then we can recover the scene in real-time. Comprehensive experiments on different types of weather/imaging conditions illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

关键词： Learning systems computer vision Visualization Imaging Estimation Training data Video surveillance

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 << < 488 489 490 491 492 493 494 495 496 497 > >>

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：