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

29,393 篇 会议
1,358 册 图书
225 篇 期刊文献

馆藏范围

30,974 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

17,280 篇 工学
- 13,630 篇 计算机科学与技术...
- 5,193 篇 软件工程
- 2,983 篇 机械工程
- 2,643 篇 光学工程
- 1,421 篇 控制科学与工程
- 1,411 篇 电气工程
- 1,344 篇 信息与通信工程
- 656 篇 生物工程
- 577 篇 仪器科学与技术
- 513 篇 生物医学工程（可授...
- 468 篇 电子科学与技术（可...
- 251 篇 化学工程与技术
- 212 篇 安全科学与工程
- 140 篇 交通运输工程
- 132 篇 建筑学
- 123 篇 材料科学与工程（可...
- 119 篇 土木工程
5,054 篇 理学
- 3,127 篇 物理学
- 2,406 篇 数学
- 824 篇 生物学
- 802 篇 统计学（可授理学、...
- 299 篇 系统科学
- 228 篇 化学
3,831 篇 医学
- 3,799 篇 临床医学
- 185 篇 基础医学(可授医学...
- 140 篇 药学(可授医学、理...
1,059 篇 管理学
- 617 篇 图书情报与档案管...
- 467 篇 管理科学与工程(可...
- 145 篇 工商管理
373 篇 艺术学
- 373 篇 设计学（可授艺术学...
116 篇 法学
81 篇 农学
48 篇 教育学
43 篇 经济学
18 篇 军事学
8 篇 文学

主题

12,602 篇 computer vision
5,697 篇 pattern recognit...
3,180 篇 training
2,263 篇 cameras
2,178 篇 computational mo...
2,116 篇 feature extracti...
2,048 篇 image segmentati...
1,970 篇 visualization
1,967 篇 shape
1,642 篇 robustness
1,493 篇 layout
1,476 篇 three-dimensiona...
1,445 篇 computer science
1,338 篇 computer archite...
1,296 篇 object detection
1,220 篇 semantics
1,142 篇 face recognition
1,107 篇 conferences
1,077 篇 benchmark testin...
1,056 篇 humans

机构

137 篇 univ sci & techn...
134 篇 tsinghua univers...
134 篇 univ chinese aca...
118 篇 chinese univ hon...
101 篇 microsoft resear...
97 篇 zhejiang univers...
94 篇 national laborat...
93 篇 shanghai jiao to...
93 篇 zhejiang univ pe...
85 篇 university of sc...
79 篇 shanghai ai lab ...
78 篇 swiss fed inst t...
65 篇 microsoft res as...
62 篇 adobe research
62 篇 computer vision ...
61 篇 peking univ peop...
58 篇 univ oxford oxfo...
57 篇 google mountain ...
57 篇 hong kong univ s...
56 篇 google res mount...

作者

107 篇 umapada pal
81 篇 van gool luc
68 篇 zhang lei
59 篇 timofte radu
41 篇 yang yi
37 篇 loy chen change
37 篇 hanqing lu
33 篇 liu yang
33 篇 xiaoou tang
32 篇 nassir navab
32 篇 wang liang
30 篇 tian qi
29 篇 h. bischof
29 篇 jan-michael frah...
29 篇 vittorio murino
29 篇 darrell trevor
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 murino vittorio
27 篇 chen chen

语言

30,833 篇 英文
92 篇 中文
73 篇 其他
6 篇 土耳其文
2 篇 日文
2 篇 俄文
1 篇 西班牙文

检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30976 条记录，以下是4701-4710 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：

Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and recognition

Coupled Dictionary and Feature Space Learning with Applicati...

引用

IEEE International conference on computer vision (ICCV)

作者： Huang, De-An Wang, Yu-Chiang Frank Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan

ISBN: (纸本)9781479928392

Cross-domain image synthesis and recognition are typically considered as two distinct tasks in the areas of computer vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be easily generalized or extended for solving the other. In this paper, we propose a unified model for coupled dictionary and feature space learning. The proposed learning model not only observes a common feature space for associating cross-domain image data for recognition purposes, the derived feature space is able to jointly update the dictionaries in each image domain for improved representation. This is why our method can be applied to both cross-domain image synthesis and recognition problems. Experiments on a variety of synthesis and recognition tasks such as single image super-resolution, cross-view action recognition, and sketch-to-photo face recognition would verify the effectiveness of our proposed learning model.

关键词： Eigenvector recognition (Psychology) images Image synthesis Dictionaries as Topic computer vision Learning

来源：评论

学校读者我要写书评

暂无评论

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Look Closer to Segment Better: Boundary Patch Refinement for...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin Tsinghua Univ THU Bosch JCML Ctr State Key Lab Intelligent Technol & Syst Dept Comp Sci & TechnolInst AIBNRist Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China

ISBN: (纸本)9781665445092

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the "PolyTransform + SegFix" baseline, we reached 1st place on the Cityscapes leaderboard. Code is available at https://***/tinyalpha/BPR.

关键词： Measurement computer vision Codes Benchmark testing Business process re-engineering pattern recognition Spatial resolution

来源：评论

学校读者我要写书评

暂无评论

Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations 32

Devil is in the Edges: Learning Semantic Boundaries from Noi...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Acuna, David Kar, Amlan Fidler, Sanja NVIDIA Toronto ON Canada Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781728132938

We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting the fact that precise annotations are laborious to get and thus annotators trade-off quality with efficiency. We aim to learn sharp and precise semantic boundaries by explicitly reasoning about annotation noise during training. We propose a simple new layer and loss that can be used with existing learning-based boundary detectors. Our layer/loss enforces the detector to predict a maximum response along the normal direction at an edge, while also regularizing its direction. We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion. Experiments show that we improve over the CASENet [36] backbone network by more than 4% in terms of MF(ODS) and 18.61% in terms of AP, outperforming all current state-of-the-art methods including those that deal with alignment. Furthermore, we show that our learned network can be used to significantly improve coarse segmentation labels, lending itself as an efficient way to label new data.

关键词： Grouping and Shape Low-level vision Segmentation

来源：评论

学校读者我要写书评

暂无评论

Boundary IoU: Improving Object-Centric Image Segmentation Evaluation

Boundary IoU: Improving Object-Centric Image Segmentation Ev...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Bowen Girshick, Ross Dollar, Piotr Berg, Alexander C. Kirillov, Alexander UIUC Urbana IL 61820 USA Facebook AI Res FAIR Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects. The new quality measure displays several desirable characteristics like symmetry w. r. t. prediction/ground truth pairs and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other boundary-focused measures like Trimap IoU and F-measure. Based on Boundary IoU, we update the standard evaluation protocols for instance and panoptic segmentation tasks by proposing the Boundary AP (Average Precision) and Boundary PQ (Panoptic Quality) metrics, respectively. Our experiments show that the new evaluation metrics track boundary quality improvements that are generally overlooked by current Mask Jo U-based evaluation metrics. We hope that the adoption of the new boundary-sensitive evaluation metrics will lead to rapid progress in segmentation methods that improve boundary quality.

关键词： Image segmentation Protocols Measurement uncertainty Tools Size measurement Performance analysis pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Towards Total Recall in Industrial Anomaly Detection

Towards Total Recall in Industrial Anomaly Detection

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Roth, Karsten Pemula, Latha Zepeda, Joaquin Scholkopf, Bernhard Brox, Thomas Gehler, Peter Univ Tubingen Tubingen Germany Amazon AWS Seattle WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best peforming approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose PatchCore, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the challenging, widely used MVTec AD benchmark PatchCore achieves an image-level anomaly detection AUROC score of up to 99.6%, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime. Code: ***/amazon-research/patchcore-inspection.

关键词： Location awareness Training Runtime Memory management Benchmark testing Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

ACRE: Abstract Causal REasoning Beyond Covariation

ACRE: Abstract Causal REasoning Beyond Covariation

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Chi Jia, Baoxiong Edmonds, Mark Zhu, Song-Chun Zhu, Yixin UCLA Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA

ISBN: (纸本)9781665445092

Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Causal REasoning (ACRE) dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario: direct, indirect, screening-off, and backward-blocking, intentionally going beyond the simple strategy of inducing causal relationships by covariation. By analyzing visual reasoning architectures on this testbed, we notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning. These deficiencies call for future research in models with a more comprehensive capability of causal induction.

关键词： Visualization Pediatrics Systematics Benchmark testing Particle measurements Cognition pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision

Contextualized Spatio-Temporal Contrastive Learning with Sel...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yuan, Liangzhe Qian, Rui Cui, Yin Gong, Boqing Schroff, Florian Yang, Ming-Hsuan Adam, Hartwig Liu, Ting Google Res Mountain View CA 94043 USA Cornell Univ Ithaca NY 14853 USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Modern self:supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes suboptimal for learning spatio-temporally fine-grained features in videos, where scenes and instances evolve through space and time. In this paper;we present Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively learn spatio-temporally fine-grained video representations via self-supervision. We first design a region-based pretext task which requires the model to transform instance representations from one view to another, guided by context features. Further;we introduce a simple network design that successfully reconciles the simultaneous learning process of both holistic and local representations. We evaluate our learned representations on a variety of downstream tasks and show that ConST-CL achieves competitive results on 6 datasets, including Kinetics, UCF, HMDB, AVA-Kinetics, AVA and OTB. Our code and models will be available at https : //***/tensorflow/models/tree/master/official/projects/const_c1.

关键词： Representation learning Location awareness Self-supervised learning Transforms pattern recognition Spatiotemporal phenomena Kinetic theory

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Person Image Generation with Semantic Parsing Transformation 32

Unsupervised Person Image Generation with Semantic Parsing T...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Song, Sijie Zhang, Wei Liu, Jiaying Mei, Tao Peking Univ Inst Comp Sci & Technol Beijing Peoples R China JD AI Res Beijing Peoples R China

ISBN: (纸本)9781728132938

In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we propose a new pathway to decompose the hard mapping into two more accessible subtasks, namely, semantic parsing transformation and appearance generation. Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning. Secondly, an appearance generative network learns to synthesize semantic-aware textures. Thirdly, we demonstrate that training our framework in an end-to-end manner further refines the semantic maps and final results accordingly. Our method is generalizable to other semantic-aware person image generation tasks, e.g., clothing texture transfer and controlled image manipulation. Experimental results demonstrate the superiority of our method on DeepFashion and Market-1501 datasets, especially in keeping the clothing attributes and better body shapes.

关键词： Image and Video Synthesis vision Applications and Systems

来源：评论

学校读者我要写书评

暂无评论

Continual Stereo Matching of Continuous Driving Scenes with Growing Architecture

Continual Stereo Matching of Continuous Driving Scenes with ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Chenghao Tian, Kun Fan, Bin Meng, Gaofeng Zhang, Zhaoxiang Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China HK Inst Sci & Innovat CAS Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The deep stereo models have achieved state-of-the-art performance on driving scenes, but they suffer from severe performance degradation when tested on unseen scenes. Although recent work has narrowed this performance gap through continuous online adaptation, this setup requires continuous gradient updates at inference and can hardly deal with rapidly changing scenes. To address these challenges, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at deployment. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth for continual learning of new scenes. During growth, it can maintain high reusability by reusing previous neural units while achieving good performance. A module named Scene Router is further introduced to adaptively select the scene-specific architecture path at inference. Experimental results demonstrate that our method achieves compelling performance in various types of challenging driving scenes.

关键词： Degradation Adaptation models computer vision computer architecture Predictive models Task analysis pattern matching

来源：评论

学校读者我要写书评

暂无评论

Using Unknown Occluders to Recover Hidden Scenes 32

Using Unknown Occluders to Recover Hidden Scenes

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yedidia, Adam B. Baradad, Manel Thrampoulidis, Christos Freeman, William T. Wornell, Gregory W. MIT Cambridge MA 02139 USA UC Santa Barbara Santa Barbara CA USA Google Res Mountain View CA USA

ISBN: (纸本)9781728132938

We consider the challenging problem of inferring a hidden moving scene from faint shadows cast on a diffuse surface. Recent work in passive non-line-of-sight (NLoS) imaging has shown that the presence of occluding objects in between the scene and the diffuse surface significantly improves the conditioning of the problem I I. However, that work assumes that the shape of the occluder is known a priori. In this paper, we relax this often impractical assumption, extending the range of applications for passive occluder-based NLoS imaging systems. We formulate the task of jointly recovering the unknown scene and unknown occluder as a blind deconvolution problem, for which we propose a simple but effective two-step algorithm. At the first step, the algorithm exploits motion in the scene in order to obtain an estimate of the occluder. In particular, it exploits the fact that motion in realistic scenes is typically sparse. The second step is more standard: using regularization, we deconvolve by the occluder estimate to solve for the hidden scene. We demonstrate the effectiveness of our method with simulations and experiments in a variety of settings.

关键词： Computational Photography Scene Analysis and Understanding vision Applications and Systems

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 << < 467 468 469 470 471 472 473 474 475 476 > >>

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：