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

22,774 篇 会议
111 篇 期刊文献
23 册 图书

馆藏范围

22,907 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,400 篇 工学
- 10,881 篇 计算机科学与技术...
- 3,451 篇 软件工程
- 2,430 篇 机械工程
- 1,722 篇 光学工程
- 1,011 篇 控制科学与工程
- 997 篇 电气工程
- 762 篇 信息与通信工程
- 394 篇 仪器科学与技术
- 337 篇 生物工程
- 257 篇 生物医学工程（可授...
- 215 篇 电子科学与技术（可...
- 113 篇 化学工程与技术
- 113 篇 安全科学与工程
- 98 篇 测绘科学与技术
- 94 篇 交通运输工程
- 86 篇 建筑学
- 82 篇 土木工程
3,362 篇 医学
- 3,348 篇 临床医学
- 79 篇 基础医学(可授医学...
3,252 篇 理学
- 1,953 篇 物理学
- 1,665 篇 数学
- 567 篇 统计学（可授理学、...
- 484 篇 生物学
- 245 篇 系统科学
- 110 篇 化学
507 篇 管理学
- 299 篇 图书情报与档案管...
- 220 篇 管理科学与工程(可...
- 75 篇 工商管理
252 篇 艺术学
- 252 篇 设计学（可授艺术学...
62 篇 法学
- 59 篇 社会学
40 篇 农学
25 篇 教育学
19 篇 经济学
11 篇 军事学
3 篇 文学

主题

10,127 篇 computer vision
4,025 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,792 篇 cameras
1,759 篇 visualization
1,485 篇 shape
1,466 篇 image segmentati...
1,448 篇 feature extracti...
1,412 篇 three-dimensiona...
1,288 篇 robustness
1,169 篇 computer archite...
1,144 篇 layout
1,141 篇 computer science
1,134 篇 semantics
1,070 篇 object detection
1,043 篇 conferences
1,009 篇 benchmark testin...
967 篇 codes
810 篇 face recognition

机构

135 篇 univ sci & techn...
118 篇 univ chinese aca...
118 篇 chinese univ hon...
110 篇 carnegie mellon ...
99 篇 tsinghua univers...
99 篇 microsoft resear...
94 篇 swiss fed inst t...
92 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 university of ch...
72 篇 shanghai jiao to...
68 篇 microsoft res as...
65 篇 national laborat...
65 篇 alibaba grp peop...
64 篇 tsinghua univ pe...
63 篇 adobe research
60 篇 peking univ peop...
59 篇 peng cheng labor...

作者

78 篇 van gool luc
72 篇 timofte radu
63 篇 zhang lei
45 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
33 篇 xiaoou tang
33 篇 li stan z.
33 篇 qi tian
32 篇 sun jian
31 篇 liu yang
31 篇 li fei-fei
30 篇 chen chen
30 篇 tian qi
30 篇 pascal fua
29 篇 darrell trevor
28 篇 ying shan
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 hanqing lu

语言

22,831 篇 英文
50 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4411-4420 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：

Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

Affect2MM: Affective Analysis of Multimedia Content Using Em...

引用

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

作者： Mittal, Trisha Mathur, Puneet Bera, Aniket Manocha, Dinesh Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

ISBN: (纸本)9781665445092

We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine the emotional state evoked in clips of movies. Affect2MM explicitly models the temporal causality using attention-based methods and Granger causality. We use a variety of components like facial features of actors involved, scene understanding, visual aesthetics, action/situation description, and movie script to obtain an affective-rich representation to understand and perceive the scene. We use an LSTM-based learning model for emotion perception. To evaluate our method, we analyze and compare our performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE dataset, and observe an average of 10 - 15% increase in the performance over SOTA methods for all three datasets.

关键词： Learning systems Visualization computer vision Computational modeling Motion pictures pattern recognition Facial features

来源：评论

学校读者我要写书评

暂无评论

Learning Multi-Scale Photo Exposure Correction

Learning Multi-Scale Photo Exposure Correction

引用

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

作者： Afifi, Mahmoud Derpanis, Konstantinos G. Ommer, Bjoern Brown, Michael S. Samsung AI Ctr SAIC Toronto ON Canada York Univ N York ON Canada Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665445092

Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each subproblem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.

关键词： Deep learning Visualization computer vision Image color analysis Cameras pattern recognition Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow

Progressively Complementary Network for Fisheye Image Rectif...

引用

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

作者： Yang, Shangrong Lin, Chunyu Liao, Kang Zhang, Chunjie Zhao, Yao Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9781665445092

Distortion rectification is often required for fisheye images. The generation-based method is one mainstream solution due to its label-free property, but its naive skip-connection and overburdened decoder will cause blur and incomplete correction. First, the skip-connection directly transfers the image features, which may introduce distortion and cause incomplete correction. Second, the decoder is overburdened during simultaneously reconstructing the content and structure of the image, resulting in vague performance. To solve these two problems, in this paper, we focus on the interpretable correction mechanism of the distortion rectification network and propose a feature-level correction scheme. We embed a correction layer in skip-connection and leverage the appearance flows in different layers to pre-correct the image features. Consequently, the decoder can easily reconstruct a plausible result with the remaining distortion-less information. In addition, we propose a parallel complementary structure. It effectively reduces the burden of the decoder by separating content reconstruction and structure correction. Subjective and objective experiment results on different datasets demonstrate the superiority of our method.

关键词： computer vision Distortion Decoding pattern recognition Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Visualizing Adapted Knowledge in Domain Transfer

Visualizing Adapted Knowledge in Domain Transfer

引用

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

作者： Hou, Yunzhong Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data.

关键词： Adaptation models computer vision Computational modeling Force Data visualization Data models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Spatially-Varying Outdoor Lighting Estimation from Intrinsics

Spatially-Varying Outdoor Lighting Estimation from Intrinsic...

引用

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

作者： Zhu, Yongjie Zhang, Yinda Li, Si Shi, Boxin Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Google Mountain View CA 94043 USA Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

We present SOLID-Net, a neural network for spatially-varying outdoor lighting estimation from a single outdoor image for any 2D pixel location. Previous work has used a unified sky environment map to represent outdoor lighting. Instead, we generate spatially-varying local lighting environment maps by combining global sky environment map with warped image information according to geometric information estimated from intrinsics. As no outdoor dataset with image and local lighting ground truth is readily available, we introduce the SOLID-Img dataset with physically-based rendered images and their corresponding intrinsic and lighting information. We train a deep neural network to regress intrinsic cues with physically-based constraints and use them to conduct global and local lightings estimation. Experiments on both synthetic and real datasets show that SOLID-Net significantly outperforms previous methods.

关键词： Deep learning computer vision Neural networks Lighting Estimation pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Cascaded Prediction Network via Segment Tree for Temporal Video Grounding

Cascaded Prediction Network via Segment Tree for Temporal Vi...

引用

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

作者： Zhao, Yang Zhao, Zhou Zhang, Zhu Lin, Zhijie Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods.

关键词： computer vision Costs Grounding Navigation Fuses Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

Bilevel Online Adaptation for Out-of-Domain Human Mesh Recon...

引用

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

作者： Guan, Shanyan Xu, Jingwei Wang, Yunbo Ni, Bingbing Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper considers a new problem of adapting a pretrained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model [36] underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks(1).

关键词： Training Adaptation models computer vision Benchmark testing Reconstruction algorithms Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

Modular Interactive Video Object Segmentation: Interaction-t...

引用

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

作者： Cheng, Ho Kei Tai, Yu-Wing Tang, Chi-Keung UIUC Champaign IL 61820 USA HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-k filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research.

关键词： computer vision Codes Fuses Filtering Object segmentation pattern recognition Sparks

来源：评论

学校读者我要写书评

暂无评论

Taskology: Utilizing Task Relations at Scale

Taskology: Utilizing Task Relations at Scale

引用

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

作者： Lu, Yao Pirk, Soren Dlabal, Jan Brohan, Anthony Pasad, Ankita Chen, Zhao Casser, Vincent Angelova, Anelia Gordon, Ariel Google Robot Mountain View CA 94043 USA Google Res Mountain View CA 94043 USA Toyota Technol Inst Chicago Chicago IL 60637 USA Waymo LLC Mountain View CA USA

ISBN: (纸本)9781665445092

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other through their known relationships via consistency losses. Furthermore, explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data, and allows training with additional unsupervised or simulated data. We demonstrate a distributed joint training algorithm with task-level parallelism, which affords a high degree of asynchronicity and robustness. This allows learning across multiple tasks, or with large amounts of input data, at scale. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and egomotion estimation, and object tracking and 3D detection in point clouds. We observe improved performance across these tasks, especially in the low-label regime.

关键词： Training computer vision Three-dimensional displays Computational modeling Semantics Estimation Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Human De-occlusion: Invisible Perception and Recovery for Humans

Human De-occlusion: Invisible Perception and Recovery for Hu...

引用

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

作者： Zhou, Qiang Wang, Shiyin Wang, Yitong Huang, Zilong Wang, Xinggang Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we tackle the problem of human deocclusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage framework is proposed to estimate the invisible portions and recover the content inside. For the stage of mask completion, a stacked network structure is devised to refine inaccurate masks from a general instance segmentation model and predict integrated masks simultaneously. Additionally, the guidance from human parsing and typical pose masks are leveraged to bring prior information. For the stage of content recovery, a novel parsing guided attention module is applied to isolate body parts and capture context information across multiple scales. Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion. AHP has advantages of providing annotations from real-world scenes and the number of humans is comparatively larger than other amodal perception datasets. Based on this dataset, experiments demonstrate that our method performs over the state-of-the-art techniques in both tasks of mask completion and content recovery. Our AHP dataset is available at https://***/ahp/.

关键词： computer vision Annotations Aggregates Refining Predictive models pattern recognition 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 << < 438 439 440 441 442 443 444 445 446 447 > >>

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：