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

12,844 篇 会议
13 篇 期刊文献
2 册 图书

馆藏范围

12,859 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,573 篇 工学
- 6,863 篇 计算机科学与技术...
- 880 篇 机械工程
- 814 篇 软件工程
- 435 篇 控制科学与工程
- 360 篇 光学工程
- 306 篇 电气工程
- 209 篇 仪器科学与技术
- 124 篇 信息与通信工程
- 91 篇 生物工程
- 62 篇 生物医学工程（可授...
- 39 篇 电子科学与技术（可...
- 34 篇 安全科学与工程
- 26 篇 化学工程与技术
- 21 篇 交通运输工程
- 20 篇 建筑学
- 18 篇 土木工程
2,957 篇 医学
- 2,956 篇 临床医学
- 15 篇 基础医学(可授医学...
- 12 篇 药学(可授医学、理...
700 篇 理学
- 359 篇 物理学
- 225 篇 数学
- 175 篇 系统科学
- 95 篇 统计学（可授理学、...
- 93 篇 生物学
- 22 篇 化学
201 篇 艺术学
- 201 篇 设计学（可授艺术学...
84 篇 管理学
- 59 篇 图书情报与档案管...
- 25 篇 管理科学与工程(可...
- 14 篇 工商管理
23 篇 法学
- 21 篇 社会学
5 篇 农学
4 篇 教育学
2 篇 经济学
1 篇 军事学

主题

6,464 篇 computer vision
2,688 篇 training
2,437 篇 pattern recognit...
1,780 篇 computational mo...
1,522 篇 visualization
1,348 篇 three-dimensiona...
1,091 篇 computer archite...
1,063 篇 semantics
997 篇 benchmark testin...
976 篇 codes
970 篇 conferences
854 篇 feature extracti...
830 篇 cameras
771 篇 task analysis
707 篇 deep learning
646 篇 image segmentati...
611 篇 object detection
595 篇 shape
554 篇 transformers
538 篇 neural networks

机构

132 篇 univ sci & techn...
122 篇 carnegie mellon ...
120 篇 tsinghua univ pe...
114 篇 univ chinese aca...
113 篇 chinese univ hon...
94 篇 tsinghua univers...
91 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 peng cheng lab p...
81 篇 university of ch...
80 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 peng cheng labor...
75 篇 university of sc...
69 篇 shanghai jiao to...
68 篇 shanghai jiao to...
67 篇 alibaba grp peop...
67 篇 stanford univ st...
66 篇 univ hong kong p...
64 篇 sensetime res pe...

作者

77 篇 timofte radu
63 篇 van gool luc
45 篇 zhang lei
36 篇 yang yi
36 篇 luc van gool
34 篇 tao dacheng
31 篇 loy chen change
29 篇 chen chen
28 篇 sun jian
28 篇 qi tian
25 篇 li xin
24 篇 liu yang
24 篇 tian qi
24 篇 ying shan
23 篇 wang xinchao
23 篇 zha zheng-jun
23 篇 boxin shi
21 篇 zhou jie
21 篇 vasconcelos nuno
20 篇 luo ping

语言

12,851 篇 英文
7 篇 其他
1 篇 中文

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

共 12859 条记录，以下是4761-4770 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：

QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information

QPIC: Query-Based Pairwise Human-Object Interaction Detectio...

引用

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

作者： Tamura, Masato Ohashi, Hiroki Yoshinaga, Tomoaki Hitachi Ltd Lumada Data Sci Lab Tokyo Japan Hitachi Ltd Ctr Technol Innovat Artificial Intelligence Tokyo Japan

ISBN: (纸本)9781665445092

We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based methods face the following three major drawbacks;they cannot leverage image-wide features due to CNN's locality, they rely on a manually defined location-of-interest for the feature aggregation, which sometimes does not cover contextually important regions, and they cannot help but mix up the features for multiple HOI instances if they are located closely. To overcome these drawbacks, we propose a transformer-based feature extractor, in which an attention mechanism and query-based detection play key roles. The attention mechanism is effective in aggregating contextually important information image-wide, while the queries, which we design in such a way that each query captures at most one human-object pair, can avoid mixing up the features from multiple instances. This transformer-based feature extractor produces so effective embeddings that the subsequent detection heads may be fairly simple and intuitive. The extensive analysis reveals that the proposed method successfully extracts contextually important features, and thus outperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.6 mAP on V-COCO).

关键词： computer vision Graphical models Codes Aggregates Detectors Feature extraction Transformers

来源：评论

学校读者我要写书评

暂无评论

Pixel-aligned Volumetric Avatars

Pixel-aligned Volumetric Avatars

引用

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

作者： Raj, Amit Zollhofer, Michael Simon, Tomas Saragih, Jason Saito, Shunsuke Hays, James Lombardi, Stephen Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus side-stepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision. We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting.

关键词： Hair computer vision Head Three-dimensional displays Telepresence Avatars Neural networks

来源：评论

学校读者我要写书评

暂无评论

Generic Perceptual Loss for Modeling Structured Output Dependencies

Generic Perceptual Loss for Modeling Structured Output Depen...

引用

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

作者： Liu, Yifan Chen, Hao Chen, Yu Yin, Wei Shen, Chunhua Univ Adelaide Adelaide SA Australia Automind Vancouver BC Canada Monash Univ Clayton Vic Australia

ISBN: (纸本)9781665445092

The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution [16], and style transfer [14]. It was believed that the success lies in the high-level perceptual feature representations extracted from CNNs pretrained with a large set of images. Here we reveal that, what matters is the network structure instead of the trained weights. Without any learning, the structure of a deep network is sufficient to capture the dependencies between multiple levels of variable statistics using multiple layers of CNNs. This insight removes the requirements of pre-training and a particular network structure (commonly, VGG) that are previously assumed for the perceptual loss, thus enabling a significantly wider range of applications. To this end, we demonstrate that a randomly-weighted deep CNN can be used to model the structured dependencies of outputs. On a few dense perpixel prediction tasks such as semantic segmentation, depth estimation and instance segmentation, we show improved results of using the extended randomized perceptual loss, compared to the baselines using pixel-wise loss alone. We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.

关键词： Training computer vision Image segmentation Image synthesis Semantics Superresolution Estimation

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

RPN Prototype Alignment For Domain Adaptive Object Detector

RPN Prototype Alignment For Domain Adaptive Object Detector

引用

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

作者： Zhang, Yixin Wang, Zilei Mao, Yushi Univ Sci & Technol China Dept Automat Hefei Peoples R China

ISBN: (纸本)9781665445092

Recent years have witnessed great progress of object detection. However, due to the domain shift problem, applying the knowledge of an object detector learned from one specific domain to another one often suffers severe performance degradation. Most existing methods adopt feature alignment either on the backbone network or instance classifier to increase the transferability of object detector. Differently, we propose to perform feature alignment in the RPN stage such that the foreground and background RPN proposals in target domain can be effectively distinguished. Specifically, we first construct one set of learnable RPN prototpyes, and then enforce the RPN features to align with the prototypes for both source and target domains. It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features. Particularly, we propose a simple yet effective method suitable for RPN feature alignment to generate high-quality pseudo label of proposals in target domain, i.e., using the filtered detection results with IoU. Furthermore, we adopt Grad CAM to find the discriminative region within a foreground proposal and use it to increase the discriminability of RPN features for alignment. We conduct extensive experiments on multiple cross-domain detection scenarios, and the results show the effectiveness of our proposed method against previous stateof-the-art methods.

关键词： Degradation computer vision Prototypes Detectors Object detection Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Patch-VQ: 'Patching Up' the Video Quality Problem

Patch-VQ: 'Patching Up' the Video Quality Problem

引用

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

作者： Ying, Zhenqiang Mandal, Maniratnam Ghadiyaram, Deepti Bovik, Alan Univ Texas Austin Austin TX 78712 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem for social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, "in-the-wild" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 38, 811 real-world distorted videos and 116, 433 space-time localized video patches ('v-patches'), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time. The entire dataset and prediction models are freely available at https://***/***..

关键词： User-generated content Streaming media Predictive models Distortion Data models Quality assessment pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Interactive Self-Training with Mean Teachers for Semi-supervised Object Detection

Interactive Self-Training with Mean Teachers for Semi-superv...

引用

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

作者： Yang, Qize Wei, Xihan Wang, Biao Hua, Xian-Sheng Zhang, Lei Alibaba Grp Hangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

The goal of semi-supervised object detection is to learn a detection model using only a few labeled data and large amounts of unlabeled data, thereby reducing the cost of data labeling. Although a few studies have proposed various self-training-based methods or consistency regularization-based methods, they ignore the discrepancies among the detection results in the same image that occur during different training iterations. Additionally, the predicted detection results vary among different detection models. In this paper, we propose an interactive form of self-training using mean teachers for semi-supervised object detection. Specifically, to alleviate the instability among the detection results in different iterations, we propose using nonmaximum suppression to fuse the detection results from different iterations. Simultaneously, we use multiple detection heads that predict pseudo labels for each other to provide complementary information. Furthermore, to avoid different detection heads collapsing to each other, we use a mean teacher model instead of the original detection model to predict the pseudo labels. Thus, the object detection model can be trained on both labeled and unlabeled data. Extensive experimental results verify the effectiveness of our proposed method.

关键词： Training computer vision Head Costs Fuses Object detection Predictive models

来源：评论

学校读者我要写书评

暂无评论

Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

来源：评论

学校读者我要写书评

暂无评论

A Dual Iterative Refinement Method for Non-rigid Shape Matching

A Dual Iterative Refinement Method for Non-rigid Shape Match...

引用

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

作者： Xiang, Rui Lai, Rongjie Zhao, Hongkai UC Dept Math Irvine CA 92697 USA Rensselaer Polytech Inst Dept Math Troy NY 12181 USA Duke Univ Dept Math Durham NC 27706 USA

ISBN: (纸本)9781665445092

In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration. In each DIR iteration, starting from current correspondence, a zoom-in process at each point is used to select well matched anchor pairs by a local mapping distortion criterion. These selected anchor pairs are then used to align spectral features (or other appropriate global features) whose dimension adaptively matches the capacity of the selected anchor pairs. Thanks to the effective combination of complementary information in a data-adaptive way, DIR is not only efficient but also robust to render accurate results within a few iterations. By choosing appropriate dual features, DIR has the flexibility to handle patch and partial matching as well. Our comprehensive experiments on various data sets demonstrate the superiority of DIR over other state-of-the-art methods in terms of both accuracy and efficiency.

关键词： computer vision Image analysis Shape Stability criteria Feature extraction Distortion Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Shadow Removal via Global Residual Free Unet and Shadow Generation

Shadow Removal via Global Residual Free Unet and Shadow Gene...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dong Li Xin Lu Yurui Zhu Xi Wang Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing shadow removal methods face challenges when confronted with real-world scenes, particularly when dealing with complex background information and high-resolution images. To address these issues, we present a shadow removal framework based on shadow generation and Global Residual Free Unet (GRFUnet), which improves shadow removal from both data and network perspectives. For data enhancement, we train a generative adversarial network for producing shadow masks, which are then multiplied with clean images to obtain new shadow images. The additional shadow data produced by this structured generation approach allows for ample constraining of the network in color aspects, thereby enhancing color consistency in the images. In terms of network architecture, we design the Global Residual Free Unet that employs a convolutional framework to sequentially conduct Spatial Interaction and Channel Evolution for effective feature extraction. Moreover, we eliminate the commonly used Global residual connections in image restoration, as we discern their in-effectiveness for the non-additive task of shadow removal. Through this methodology, we achieve excellent shadow removal results, both qualitatively and quantitatively. Our method achieves the highest PSNR in the NTIRE24-Image Shadow Removal Challenge and achieves commendable and balanced outcomes across two tracks—placing third in the fidelity track and fourth in the perceptual track.

关键词： computer vision Image color analysis conferences computer architecture Network architecture Feature extraction Generative adversarial networks

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 << < 473 474 475 476 477 478 479 480 481 482 > >>

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：