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

6,810 篇 会议
36 篇 期刊文献
12 册 图书

馆藏范围

6,857 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,042 篇 工学
- 3,808 篇 计算机科学与技术...
- 1,615 篇 软件工程
- 847 篇 光学工程
- 383 篇 信息与通信工程
- 286 篇 控制科学与工程
- 212 篇 机械工程
- 189 篇 电气工程
- 97 篇 生物医学工程（可授...
- 80 篇 电子科学与技术（可...
- 79 篇 生物工程
- 62 篇 仪器科学与技术
- 39 篇 力学（可授工学、理...
- 38 篇 建筑学
- 37 篇 土木工程
- 34 篇 航空宇航科学与技...
- 29 篇 安全科学与工程
- 24 篇 测绘科学与技术
- 23 篇 交通运输工程
- 22 篇 化学工程与技术
1,618 篇 理学
- 1,021 篇 物理学
- 1,005 篇 数学
- 379 篇 统计学（可授理学、...
- 160 篇 生物学
- 38 篇 系统科学
- 27 篇 化学
185 篇 管理学
- 132 篇 图书情报与档案管...
- 56 篇 管理科学与工程(可...
- 32 篇 工商管理
153 篇 医学
- 153 篇 临床医学
- 22 篇 基础医学(可授医学...
18 篇 法学
11 篇 教育学
10 篇 农学
7 篇 经济学
2 篇 军事学
1 篇 艺术学

主题

2,394 篇 computer vision
879 篇 pattern recognit...
667 篇 cameras
663 篇 computer science
633 篇 face recognition
577 篇 layout
550 篇 image segmentati...
524 篇 conferences
520 篇 shape
479 篇 object recogniti...
470 篇 robustness
411 篇 humans
354 篇 feature extracti...
338 篇 object detection
308 篇 training
278 篇 application soft...
270 篇 image recognitio...
263 篇 lighting
241 篇 image reconstruc...
236 篇 computational mo...

机构

43 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
18 篇 the robotics ins...
17 篇 department of in...
17 篇 robotics institu...
17 篇 institute of com...
16 篇 univ sci & techn...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 computer vision ...
14 篇 school of comput...
14 篇 microsoft resear...
14 篇 school of comput...
13 篇 univ maryland co...

作者

39 篇 timofte radu
28 篇 s.k. nayar
26 篇 huang thomas s.
24 篇 xiaoou tang
24 篇 t. kanade
21 篇 t.s. huang
20 篇 van gool luc
20 篇 chellappa rama
19 篇 nayar shree k.
19 篇 t. darrell
17 篇 li stan z.
17 篇 torralba antonio
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 m. hebert
16 篇 g. healey
16 篇 heung-yeung shum
16 篇 zisserman andrew
15 篇 m. shah

语言

6,856 篇 英文
2 篇 中文
1 篇 土耳其文

检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

共 6858 条记录，以下是291-300 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：

S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction

S2F2: Single-Stage Flow Forecasting for Future Multiple Traj...

引用

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

作者： Chen, Yu-Wen Yang, Hsuan-Kung Chiu, Chu-Chi Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches in that it performs detection, Re-ID, and forecasting of multiple pedestrians at the same time. Unlike the prior approaches, the computational burden of S2F2 remains consistent even as the number of pedestrians grows. The experimental results demonstrate that S2F2 is able to outperform two conventional forecasting algorithms and a recent learning-based two-stage model [1], while maintaining its tracking performance on par with the contemporary MOT models.

关键词： computer vision Computational modeling conferences Predictive models Prediction algorithms Trajectory pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Se...

引用

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

作者： Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.

关键词： Training Image segmentation computer vision conferences Semantics Minimization pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

引用

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

作者： Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Unstructured Object Matching using Co-Salient Region Segmentation

Unstructured Object Matching using Co-Salient Region Segment...

引用

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

作者： Stoian, Ioana-Sabina Sandu, Ionut-Catalin Voinea, Daniel Popa, Alin-Ionut Amazon Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unstructured object matching is a less-explored and very challenging topic in the scientific literature. This includes matching scenarios where the context, appearance and the geometrical integrity of the objects to be matched changes drastically from one image to another (e.g. a pair of pyjamas which in one image is folded and in the other is worn by a person), making it impossible to determine a transformation which aligns the matched regions. Traditional approaches like keypoint-based feature matching perform poorly on this use case due to the high complexity in terms of viewpoint, scene context variety, background variations or high degrees of freedom concerning structural configurations. In this paper we propose a deep learning framework consisting of a twins based matching approach leveraging a co-salient region segmentation task and a cosine-similarity based region descriptor pairing technique. The importance of our proposed framework is demonstrated on a novel use case consisting of image pairs with various objects used by children. Additionally, we evaluate on Human3.6M and Market-1501, two datasets with humans depicting various appearances and kinematic configurations captured under different backgrounds.

关键词： Deep learning Image segmentation computer vision conferences Kinematics Search problems pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

引用

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

作者： Jean, Raphael St-Charles, Pierre-Luc Pirk, Soren Brodeur, Simon Menya Solut Sherbrooke PQ Canada Mila Montreal PQ Canada AMLRT Montreal PQ Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665487399

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

recognition of Freely Selected Keypoints on Human Limbs

Recognition of Freely Selected Keypoints on Human Limbs

引用

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

作者： Ludwig, Katja Kienzle, Daniel Lienhart, Rainer Univ Augsburg Machine Learning & Comp Vis Lab Augsburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose [12] architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs.

关键词： Measurement computer vision Image edge detection conferences Computational modeling Biological system modeling Pose estimation

来源：评论

学校读者我要写书评

暂无评论

PersonGONE: Image Inpainting for Automated Checkout Solution

PersonGONE: Image Inpainting for Automated Checkout Solution

引用

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

作者： Bartl, Vojtech Spanhel, Jakub Herout, Adam Brno Univ Technol Fac Informat Technol GRAPH FIT Bozetechova 1-2 Brno 61266 Czech Republic

ISBN: (纸本)9781665487399

In this paper, we present a solution for automatic checkout in a retail store as a part of AI City Challenge 2022. We propose a novel approach that uses the "removal" of unwanted objects - in this case, body parts of operating staff, which are localized and further removed from video by an image inpainting method. Afterwards, a neural network detector can detect products with a decreased detection false positive rate. A part of our solution is also automatic detection of ROI (the place where products are shown to the system). We reached 0.4167 F1-Score with 0.3704 precision and 0.4762 recall which placed us at the 7th place of AI City Challenge 2022 in corresponding Track 4. The code is made public and available on GitHub(1).

关键词： Deep learning Image segmentation Image recognition conferences Urban areas Neural networks Detectors

来源：评论

学校读者我要写书评

暂无评论

A New Non-central Model for Fisheye Calibration

A New Non-central Model for Fisheye Calibration

引用

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

作者： Tezaur, Radka Kumar, Avinash Nestares, Oscar Intel Corp Santa Clara CA 95054 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A new non-central model suitable for calibrating fisheye cameras is proposed. It is a direct extension of the popular central model developed by Scaramuzza et al., used by Matlab computer vision Toolbox fisheye calibration tool. It allows adapting existing applications that are using this central model to a non-central projection that is more accurate, especially when objects captured in the images are close to the camera, and it makes it possible to switch easily between the more accurate non-central characterization of the fisheye camera and the more convenient central approximation, as needed. It is shown that the algorithms proposed by Scaramuzza et al. for their central model can be modified to accommodate the angle dependent axial viewpoint shift. This means, besides other, that a similar process can be used for calibration involving the viewpoint shift characterization and a user-friendly calibration tool can be produced with this new non-central model that does not require the user to provide detailed lens design specifications or an educated guess for the initial parameter values. Several other improvements to the Scaramuzza's central model are also introduced, helping to improve the performance of both the central model, and its non-central extension.

关键词： Adaptation models computer vision Three-dimensional displays Computational modeling Switches Cameras Mathematical models

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Change Detection Based on Image Reconstruction Loss

Unsupervised Change Detection Based on Image Reconstruction ...

引用

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

作者： Noh, Hyeoncheol Ju, Jingi Seo, Minseok Park, Jongchan Choi, Dong-Geol Hanbat Natl Univ Daejeon South Korea SI Analyt Inc Mainz Germany Lunit Inc Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To train a change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change detection methods have been proposed, but they still require unlabeled bi-temporal images. In this paper, we propose an unsupervised change detection method based on image reconstruction loss, which uses only a single-temporal unlabeled image. The image reconstruction model was trained to reconstruct the original source image by receiving the source image and photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as input and aims to reconstruct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector demonstrated significant performance on various change detection benchmark datasets even though only a single-temporal source image was used. The code and trained models are available in https://***/cjf8899/CDRL

关键词： computer vision Codes conferences Semantics Detectors Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Ice hockey player identification via transformers and weakly supervised learning

Ice hockey player identification via transformers and weakly...

引用

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

作者： Vats, Kanav McNally, William Walters, Pascale Clausi, David A. Zelek, John S. Univ Waterloo Syst Design Engn Waterloo ON Canada Stathletes Inc St Catharines ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in natural language processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the same dataset. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player-shifts improved the player identification accuracy by 6%.

关键词： Training computer vision Optical character recognition Supervised learning Games Transformers Ice

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 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：