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

11,414 篇 会议
9 篇 期刊文献

馆藏范围

11,423 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,979 篇 工学
- 7,533 篇 计算机科学与技术...
- 805 篇 机械工程
- 401 篇 电气工程
- 390 篇 软件工程
- 231 篇 控制科学与工程
- 58 篇 光学工程
- 42 篇 生物工程
- 37 篇 信息与通信工程
- 19 篇 生物医学工程（可授...
- 14 篇 电子科学与技术（可...
- 13 篇 化学工程与技术
- 9 篇 安全科学与工程
- 7 篇 交通运输工程
- 6 篇 仪器科学与技术
- 4 篇 土木工程
- 3 篇 轻工技术与工程
3,137 篇 医学
- 3,137 篇 临床医学
- 8 篇 基础医学(可授医学...
- 5 篇 药学(可授医学、理...
- 4 篇 公共卫生与预防医...
307 篇 理学
- 200 篇 系统科学
- 63 篇 物理学
- 43 篇 生物学
- 30 篇 数学
- 15 篇 统计学（可授理学、...
- 14 篇 化学
29 篇 管理学
- 17 篇 图书情报与档案管...
- 13 篇 管理科学与工程(可...
- 5 篇 工商管理
3 篇 法学
- 3 篇 社会学
2 篇 教育学
2 篇 农学
1 篇 经济学
1 篇 艺术学

主题

5,612 篇 computer vision
2,584 篇 training
2,092 篇 pattern recognit...
1,682 篇 computational mo...
1,496 篇 visualization
1,343 篇 three-dimensiona...
1,098 篇 semantics
1,007 篇 benchmark testin...
1,005 篇 codes
927 篇 computer archite...
898 篇 deep learning
790 篇 task analysis
708 篇 feature extracti...
571 篇 conferences
563 篇 face recognition
520 篇 transformers
517 篇 neural networks
493 篇 object detection
476 篇 image segmentati...
452 篇 cameras

机构

172 篇 univ sci & techn...
150 篇 univ chinese aca...
148 篇 tsinghua univ pe...
145 篇 carnegie mellon ...
136 篇 chinese univ hon...
116 篇 peng cheng lab p...
106 篇 zhejiang univ pe...
97 篇 swiss fed inst t...
96 篇 sensetime res pe...
95 篇 tsinghua univers...
91 篇 shanghai ai lab ...
85 篇 shanghai jiao to...
83 篇 alibaba grp peop...
81 篇 peng cheng labor...
80 篇 zhejiang univers...
80 篇 stanford univ st...
78 篇 univ hong kong p...
77 篇 university of ch...
75 篇 australian natl ...
75 篇 tech univ munich...

作者

64 篇 timofte radu
60 篇 van gool luc
50 篇 zhang lei
44 篇 yang yi
39 篇 tao dacheng
35 篇 loy chen change
32 篇 tian qi
31 篇 zhou jie
31 篇 sun jian
30 篇 liu yang
29 篇 vasconcelos nuno
29 篇 qi tian
29 篇 zha zheng-jun
28 篇 chen chen
27 篇 boxin shi
26 篇 li xin
26 篇 luc van gool
26 篇 pollefeys marc
25 篇 liu xiaoming
25 篇 ying shan

语言

11,420 篇 英文
3 篇 其他

检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是131-140 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：

DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images

DARCNN: Domain Adaptive Region-based Convolutional Neural Ne...

引用

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

作者： Hsu, Joy Chiu, Wah Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled domain specific datasets for supervised tasks are often expensive to obtain, and furthermore discovery of novel distinct objects may be desirable for unbiased scientific discovery. Therefore, we propose leveraging the wealth of annotations in benchmark computer vision datasets to conduct unsupervised instance segmentation for diverse biomedical datasets. The key obstacle is thus overcoming the large domain shift from common to biomedical images. We propose a Domain Adaptive Region-based Convolutional Neural Network (DARCNN), that adapts knowledge of object definition from COCO, a large labelled vision dataset, to multiple biomedical datasets. We introduce a domain separation module, a self-supervised representation consistency loss, and an augmented pseudo-labelling stage within DARCNN to effectively perform domain adaptation across such large domain shifts. We showcase DARCNN's performance for unsupervised instance segmentation on numerous biomedical datasets.

关键词： Knowledge engineering Image segmentation computer vision Adaptive systems Annotations Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

IMODAL: creating learnable user-defined deformation models

IMODAL: creating learnable user-defined deformation models

引用

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

作者： Lacroix, Leander Charlier, Benjamin Trouve, Alain Gris, Barbara Univ Paris Saclay INSERM U1299 Gif Sur Yvette France Univ Montpellier IMAG Montpellier France ENS Paris Saclay Ctr Borelli Cachan France Sorbonne Univ CNRS LJLL Paris France

ISBN: (纸本)9781665445092

A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations. This interpolation process can generate deceiving results when the set of considered deformations is not relevant to the observed data. To overcome this issue, the framework of deformation modules allows to incorporate in the model structured deformation patterns coming from prior knowledge on the data. The goal of this article is twofold. First defining new deformation modules incorporating structures coming from the elastic properties of the objects. Second, presenting the IMODAL library allowing to perform registration through structured deformations. This library is modular: adapted priors can be easily defined by the user, several priors can be combined into a global one and various types of data can be considered such as curves, meshes or images. It can be downloaded at https://***/imodal.

关键词： Deformable models Adaptation models Interpolation computer vision Computational modeling Libraries Data models

来源：评论

学校读者我要写书评

暂无评论

Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition

Affective Processes: stochastic modelling of temporal contex...

引用

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

作者： Sanchez, Enrique Tellamekala, Mani Kumar Valstar, Michel Tzimiropoulos, Georgios Samsung AI Ctr Cambridge England Univ Nottingham Nottingham England Queen Mary Univ London London England

ISBN: (纸本)9781665445092

Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the feature level, ignoring the task-specific temporal dependencies, and fail to model context uncertainty. To alleviate these issues, we build upon the framework of Neural Processes to propose a method for apparent emotion recognition with three key novel components: (a) probabilistic contextual representation with a global latent variable model;(b) temporal context modelling using task-specific predictions in addition to features;and (c) smart temporal context selection. We validate our approach on four databases, two for Valence and Arousal estimation (SEWA and AffWild2), and two for Action Unit intensity estimation (DISFA and BP4D). Results show a consistent improvement over a series of strong baselines as well as over state-of-the-art methods.

关键词： Emotion recognition Uncertainty Face recognition Supervised learning Stochastic processes Estimation Predictive models

来源：评论

学校读者我要写书评

暂无评论

Learning to Segment Rigid Motions from Two Frames

Learning to Segment Rigid Motions from Two Frames

引用

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

作者： Yang, Gengshan Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9781665445092

Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations. To combine the best of both worlds, we propose a modular network, whose architecture is motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel. The inferred rigid motions lead to a significant improvement for depth and scene flow estimation.

关键词： computer vision Three-dimensional displays Motion segmentation Motion estimation Estimation Training data computer architecture

来源：评论

学校读者我要写书评

暂无评论

SiamMOT: Siamese Multi-Object Tracking

SiamMOT: Siamese Multi-Object Tracking

引用

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

作者： Shuai, Bing Berneshawi, Andrew Li, Xinyu Modolo, Davide Tighe, Joseph Amazon Web Serv AWS Seattle WA 98109 USA

ISBN: (纸本)9781665445092

In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT includes a motion model that estimates the instance's movement between two frames such that detected instances are associated. To explore how the motion modelling affects its tracking capability, we present two variants of Siamese tracker, one that implicitly models motion and one that models it explicitly. We carry out extensive quantitative experiments on three different MOT datasets: MOT17, TAO-person and Caltech Roadside Pedestrians, showing the importance of motion modelling for MOT and the ability of SiamMOT to substantially outperform the state-of-the-art. Finally, SiamMOT also outperforms the winners of ACM MM'20 HiEve Grand Challenge on HiEve dataset. Moreover, SiamMOT is efficient, and it runs at 17 FPS for 720P videos on a single modern GPU.

关键词： computer vision Tracking Graphics processing units pattern recognition Videos

来源：评论

学校读者我要写书评

暂无评论

Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

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

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

来源：评论

学校读者我要写书评

暂无评论

Generative Classifiers as a Basis for Trustworthy Image Classification

Generative Classifiers as a Basis for Trustworthy Image Clas...

引用

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

作者： Mackowiak, Radek Ardizzone, Lynton Kothe, Ullrich Rother, Carsten Heidelberg Univ Visual Learning Lab Heidelberg Germany

ISBN: (纸本)9781665445092

With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. We understand trustworthiness as the combination of explainability and robustness. Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities. However, this has mostly been demonstrated on simple datasets such as MNIST and CIFAR in the past. In this work, we firstly develop an architecture and training scheme that allows GCs to operate on a more relevant level of complexity for practical computer vision, namely the ImageNet challenge. Secondly, we demonstrate the immense potential of GCs for trustworthy image classification. Explainability and some aspects of robustness are vastly improved compared to feed-forward models, even when the GCs are just applied naively. While not all trustworthiness problems are solved completely, we observe that GCs are a highly promising basis for further algorithms and modifications. We release our trained model for download in the hope that it serves as a starting point for other generative classification tasks, in much the same way as pretrained ResNet architectures do for discriminative classification.

关键词： Training Deep learning computer vision Computational modeling computer architecture Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Shape and Material Capture at Home

Shape and Material Capture at Home

引用

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

作者： Lichy, Daniel Wu, Jiaye Sengupta, Soumyadip Jacobs, David W. Univ Maryland College Pk MD 20742 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few *** main technical contribution is the introduction of a recursive neural architecture, which can predict geometry and reflectance at 2(k) x 2(k) resolution given an input image at 2(k) x 2(k) and estimated geometry and reflectance from the previous step at 2(k-1) x 2(k -1). This recursive architecture, termed RecNet, is trained with 256 x256 resolution but can easily operate on 1024 x 1024 images during inference. We show that our method produces more accurate surface normal and albedo, especially in regions of specular highlights and cast shadows, compared to previous approaches, given three or fewer input images.

关键词： Reflectivity Geometry Training computer vision Image resolution Shape Neural networks

来源：评论

学校读者我要写书评

暂无评论

MetricOpt: Learning to Optimize Black-Box Evaluation Metrics

MetricOpt: Learning to Optimize Black-Box Evaluation Metrics

引用

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

作者： Huang, Chen Zhai, Shuangfei Guo, Pengsheng Susskind, Josh Apple Inc Cupertino CA 95014 USA

ISBN: (纸本)9781665445092

We study the problem of directly optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in a black-box setting where the computational details of the target metric are unknown. We achieve this by learning a differentiable value function, which maps compact task-specific model parameters to metric observations. The learned value function is easily pluggable into existing optimizers like SGD and Adam, and is effective for rapidly finetuning a pre-trained model. This leads to consistent improvements since the value function provides effective metric supervision during finetuning, and helps to correct the potential bias of loss-only supervision. MetricOpt achieves state-of-the-art performance on a variety of metrics for (image) classification, image retrieval and object detection. Solid benefits are found over competing methods, which often involve complex loss design or adaptation. MetricOpt also generalizes well to new tasks and model architectures.

关键词： Measurement Computational modeling Image retrieval computer architecture Object detection Solids pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Pose recognition with Cascade Transformers

Pose Recognition with Cascade Transformers

引用

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

作者： Li, Ke Wang, Shijie Zhang, Xiang Xu, Yifan Xu, Weijian Tu, Zhuowen Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

关键词： Heating systems Visualization computer vision Transformers pattern recognition Decoding Task analysis

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 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：