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

22,771 篇 会议
112 篇 期刊文献
23 册 图书

馆藏范围

22,905 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,398 篇 工学
- 10,880 篇 计算机科学与技术...
- 3,450 篇 软件工程
- 2,430 篇 机械工程
- 1,721 篇 光学工程
- 1,010 篇 控制科学与工程
- 998 篇 电气工程
- 761 篇 信息与通信工程
- 393 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
- 112 篇 安全科学与工程
- 98 篇 测绘科学与技术
- 92 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...
3,250 篇 理学
- 1,953 篇 物理学
- 1,664 篇 数学
- 567 篇 统计学（可授理学、...
- 484 篇 生物学
- 245 篇 系统科学
- 109 篇 化学
506 篇 管理学
- 299 篇 图书情报与档案管...
- 219 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

10,126 篇 computer vision
4,025 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,792 篇 cameras
1,758 篇 visualization
1,485 篇 shape
1,466 篇 image segmentati...
1,447 篇 feature extracti...
1,412 篇 three-dimensiona...
1,288 篇 robustness
1,169 篇 computer archite...
1,144 篇 layout
1,142 篇 computer science
1,134 篇 semantics
1,071 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

22,844 篇 英文
35 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 22906 条记录，以下是4621-4630 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：

Analyzing the Internals of Neural Radiance Fields

Analyzing the Internals of Neural Radiance Fields

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Lukas Radl Andreas Kurz Michael Steiner Markus Steinberger Graz University of Technology Austria

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density leveraging proposal network samplers. In contrast to the coarse-to-fine sampling approach with two NeRFs, this offers significant potential for acceleration using lower network capacity. Given that NeRFs utilize most of their network capacity to estimate radiance, they could store valuable density information in their parameters or their deep features. To investigate this proposition, we take one step back and analyze large, trained ReLU-MLPs used in coarse-to-fine sampling. Building on our novel activation visualization method, we find that trained NeRFs, Mip-NeRFs and proposal network samplers map samples with high density to local minima along a ray in activation feature space. We show how these large MLPs can be accelerated by transforming intermediate activations to a weight estimate, without any modifications to the training protocol or the network architecture. With our approach, we can reduce the computational requirements of trained NeRFs by up to 50% with only a slight hit in rendering quality. Extensive experimental evaluation on a variety of datasets and architectures demonstrates the effectiveness of our approach. Consequently, our methodology provides valuable insight into the inner workings of NeRFs.

关键词： Training Visualization computer vision Protocols conferences Network architecture Neural radiance field

来源：评论

学校读者我要写书评

暂无评论

Globally Optimal Relative Pose Estimation with Gravity Prior

Globally Optimal Relative Pose Estimation with Gravity Prior

引用

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

作者： Ding, Yaqing Barath, Daniel Yang, Jian Kong, Hui Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic SZTAKI MPLab Budapest Hungary Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665445092

Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions.

关键词： computer vision Measurement units Three-dimensional displays Codes Pose estimation Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer

Diverse Part Discovery: Occluded Person Re-identification wi...

引用

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

作者： Li, Yulin He, Jianfeng Zhang, Tianzhu Liu, Xiang Zhang, Yongdong Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Dongguan Univ Technol Dongguan Guangdong Peoples R China

ISBN: (纸本)9781665445092

Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods.

关键词： computer vision Prototypes computer architecture Benchmark testing Transformers pattern recognition Decoding

来源：评论

学校读者我要写书评

暂无评论

DeepI2P: Image-to-Point Cloud Registration via Deep Classification

DeepI2P: Image-to-Point Cloud Registration via Deep Classifi...

引用

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

作者： Li, Jiaxin Lee, Gim Hee Bytedance Beijing Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. Learning common feature descriptors to establish correspondences for the registration is inherently challenging due to the lack of appearance and geometric correlations across the two modalities. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem. A classification neural network is designed to label whether the projection of each point in the point cloud is within or beyond the camera frustum. These labeled points are subsequently passed into a novel inverse camera projection solver to estimate the relative pose. Extensive experimental results on Oxford Robotcar and KITTI datasets demonstrate the feasibility of our approach. Our source code is available at https://***/lijx10/DeepI2P.

关键词： computer vision Laser radar Three-dimensional displays Correlation Codes Robot vision systems Neural networks

来源：评论

学校读者我要写书评

暂无评论

DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation

DCT-Mask: Discrete Cosine Transform Mask Representation for ...

引用

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

作者： Shen, Xing Yang, Jirui Wei, Chunbo Deng, Bing Huang, Jianqiang Hua, Xiansheng Cheng, Xiaoliang Liang, Kewei Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28 x 28 binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In this paper, we propose a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector. Our method, termed DCT-Mask, could be easily integrated into most pixel-based instance segmentation methods. Without any bells and whistles, DCT-Mask yields significant gains on different frameworks, backbones, datasets, and training schedules. It does not require any pre-processing or pre-training, and almost no harm to the running speed. Especially, for higher-quality annotations and more complex backbones, our method has a greater improvement. Moreover, we analyze the performance of our method from the perspective of the quality of mask representation. The main reason why DCT-Mask works well is that it obtains a high-quality mask representation with low complexity.

关键词： Training Schedules computer vision Annotations Complexity theory pattern recognition Discrete cosine transforms

来源：评论

学校读者我要写书评

暂无评论

Tree-like Decision Distillation

Tree-like Decision Distillation

引用

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

作者： Song, Jie Zhang, Haofei Wang, Xinchao Xue, Mengqi Chen, Ying Sun, Li Tao, Dacheng Song, Mingli Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model. Prior methods achieve this by making the student imitate shallow behaviors, such as soft targets, features, or attention, of the teacher. In this paper, we argue that what really matters for distillation is the intrinsic problem-solving process captured by the teacher. By dissecting the decision process in a layer-wise manner, we found that the decision-making procedure in the teacher model is conducted in a coarse-to-fine manner, where coarse-grained discrimination (e.g., animal vs vehicle) is attained in early layers, and fine-grained discrimination (e.g., dog vs cat, car vs truck) in latter layers. Motivated by this observation, we propose a new distillation method, dubbed as Tree-like Decision Distillation (TDD), to endow the student with the same problem-solving mechanism as that of the teacher. Extensive experiments demonstrated that TDD yields competitive performance compared to state of the arts. More importantly, it enjoys better interpretability due to its interpretable decision distillation instead of dark knowledge distillation.

关键词： Knowledge engineering computer vision Cats Decision making Dogs pattern recognition Problem-solving

来源：评论

学校读者我要写书评

暂无评论

Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

Training Transformer Models by Wavelet Losses Improves Quant...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Cansu Korkmaz A. Murat Tekalp College of Engineering and KUIS AI Center Koc University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as l 1 , have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

关键词： Wavelet transforms Training Visualization Computational modeling conferences Superresolution computer architecture

来源：评论

学校读者我要写书评

暂无评论

ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

ConPro: Learning Severity Representation for Medical Images ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hong Nguyen Hoang Nguyen Melinda Chang Hieu Pham Shrikanth Narayanan Michael Pazzani University of Southern California Los Angeles United States Vinuni-Illinois Smart Health Center Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning-integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain.

关键词： Representation learning Measurement Visualization Contrastive learning Prediction algorithms Vectors pattern recognition

来源：评论

学校读者我要写书评

暂无评论

End-to-End Video Instance Segmentation with Transformers

End-to-End Video Instance Segmentation with Transformers

引用

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

作者： Wang, Yuqing Xu, Zhaoliang Wang, Xinlong Shen, Chunhua Cheng, Baoshan Shen, Hao Xia, Huaxia Meituan Beijing Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches. Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks. Code is available at: https://git . io/VisTR

关键词： Image segmentation computer vision Computational modeling Pipelines Transformer cores Transformers pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes

CT-Net: Complementary Transfering Network for Garment Transf...

引用

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

作者： Yang, Fan Lin, Guosheng Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781665445092

Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images. However, garment transfer between images with heavy misalignments or severe occlusions still remains as a challenge. In this work, we propose Complementary Transfering Network (CT-Net) to adaptively model different levels of geometric changes and transfer outfits between different people. In specific, CT-Net consists of three modules: i) A complementary warping module first estimates two complementary warpings to transfer the desired clothes in different granularities. ii) A layout prediction module is proposed to predict the target layout, which guides the preservation or generation of the body parts in the synthesized images. iii) A dynamic fusion module adaptively combines the advantages of the complementary warpings to render the garment transfer results. Extensive experiments conducted on DeepFashion dataset demonstrate that our network synthesizes high-quality garment transfer images and significantly outperforms the state-of-art methods both qualitatively and quantitatively. Our source code will be available online.

关键词： Adaptation models computer vision Codes Computational modeling Clothing Layout pattern recognition

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 << < 459 460 461 462 463 464 465 466 467 468 > >>

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：