咨询与建议

限定检索结果

文献类型

  • 20,858 篇 会议
  • 105 篇 期刊文献
  • 43 册 图书

馆藏范围

  • 21,005 篇 电子文献
  • 1 种 纸本馆藏

日期分布

学科分类号

  • 13,618 篇 工学
    • 11,054 篇 计算机科学与技术...
    • 2,651 篇 机械工程
    • 2,251 篇 软件工程
    • 914 篇 光学工程
    • 885 篇 电气工程
    • 528 篇 控制科学与工程
    • 476 篇 信息与通信工程
    • 216 篇 测绘科学与技术
    • 135 篇 生物工程
    • 127 篇 生物医学工程(可授...
    • 98 篇 电子科学与技术(可...
    • 92 篇 仪器科学与技术
    • 46 篇 安全科学与工程
    • 40 篇 建筑学
    • 40 篇 化学工程与技术
    • 39 篇 土木工程
    • 37 篇 交通运输工程
    • 35 篇 力学(可授工学、理...
    • 33 篇 航空宇航科学与技...
  • 3,494 篇 医学
    • 3,489 篇 临床医学
    • 32 篇 基础医学(可授医学...
  • 2,246 篇 理学
    • 1,144 篇 物理学
    • 1,081 篇 数学
    • 401 篇 生物学
    • 384 篇 统计学(可授理学、...
    • 245 篇 系统科学
    • 46 篇 化学
  • 343 篇 管理学
    • 176 篇 管理科学与工程(可...
    • 168 篇 图书情报与档案管...
    • 34 篇 工商管理
  • 31 篇 法学
  • 19 篇 农学
  • 15 篇 教育学
  • 8 篇 经济学
  • 5 篇 艺术学
  • 2 篇 军事学
  • 1 篇 文学

主题

  • 8,141 篇 computer vision
  • 2,886 篇 training
  • 2,839 篇 pattern recognit...
  • 1,809 篇 computational mo...
  • 1,715 篇 visualization
  • 1,492 篇 cameras
  • 1,433 篇 three-dimensiona...
  • 1,433 篇 feature extracti...
  • 1,366 篇 shape
  • 1,360 篇 face recognition
  • 1,242 篇 image segmentati...
  • 1,135 篇 robustness
  • 1,124 篇 semantics
  • 992 篇 computer archite...
  • 984 篇 object detection
  • 982 篇 layout
  • 959 篇 benchmark testin...
  • 935 篇 codes
  • 899 篇 computer science
  • 898 篇 object recogniti...

机构

  • 174 篇 univ sci & techn...
  • 158 篇 univ chinese aca...
  • 153 篇 carnegie mellon ...
  • 145 篇 chinese univ hon...
  • 109 篇 microsoft resear...
  • 103 篇 zhejiang univ pe...
  • 99 篇 swiss fed inst t...
  • 95 篇 tsinghua univers...
  • 91 篇 microsoft res as...
  • 90 篇 tsinghua univ pe...
  • 88 篇 shanghai ai lab ...
  • 81 篇 zhejiang univers...
  • 77 篇 alibaba grp peop...
  • 74 篇 hong kong univ s...
  • 73 篇 university of sc...
  • 72 篇 peking univ peop...
  • 72 篇 university of ch...
  • 68 篇 shanghai jiao to...
  • 66 篇 univ oxford oxfo...
  • 65 篇 google res mount...

作者

  • 80 篇 van gool luc
  • 70 篇 zhang lei
  • 58 篇 timofte radu
  • 48 篇 yang yi
  • 47 篇 luc van gool
  • 46 篇 xiaoou tang
  • 44 篇 tian qi
  • 43 篇 darrell trevor
  • 42 篇 loy chen change
  • 42 篇 sun jian
  • 41 篇 qi tian
  • 40 篇 li stan z.
  • 38 篇 li fei-fei
  • 37 篇 chen xilin
  • 36 篇 shan shiguang
  • 35 篇 zhou jie
  • 35 篇 vasconcelos nuno
  • 35 篇 liu yang
  • 35 篇 torralba antonio
  • 34 篇 liu xiaoming

语言

  • 20,982 篇 英文
  • 10 篇 中文
  • 5 篇 土耳其文
  • 5 篇 其他
  • 2 篇 日文
  • 2 篇 葡萄牙文
检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"
21006 条 记 录,以下是111-120 订阅
排序:
Prior-Less Compressible Structure from Motion  29
Prior-Less Compressible Structure from Motion
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Kong, Chen Lucey, Simon Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA
Many non-rigid 3D structures are not modelled well through a low-rank subspace assumption. This is problematic when it comes to their reconstruction through Structure from Motion (SfM). We argue in this paper that a m... 详细信息
来源: 评论
BORDER: An Oriented Rectangles Approach to Texture-less Object recognition  29
BORDER: An Oriented Rectangles Approach to Texture-less Obje...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Chan, Jacob Lee, Jimmy Addison Kemao, Qian Nanyang Technol Univ SCE Block N4 Nanyang Ave Singapore 639798 Singapore ASTAR Inst Infocomm Res I2R 1 Fusionopolis WayConnexis South Tower Singapore 138632 Singapore
This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition. By fusing a regional object encompassment concept with descriptor-base... 详细信息
来源: 评论
VLAD3: Encoding Dynamics of Deep Features for Action recognition  29
VLAD<SUP>3</SUP>: Encoding Dynamics of Deep Features for Act...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Li, Yingwei Li, Weixin Mahadevan, Vijay Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92103 USA
Previous approaches to action recognition with deep features tend to process video frames only within a small temporal region, and do not model long-range dynamic information explicitly. However, such information is i... 详细信息
来源: 评论
Fine-grained Image Classification by Exploring Bipartite-Graph Labels  29
Fine-grained Image Classification by Exploring Bipartite-Gra...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Zhou, Feng Lin, Yuanqing NEC Labs Princeton NJ 08540 USA
Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search... 详细信息
来源: 评论
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle recognition  29
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Veh...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Sochor, Jakub Herout, Adam Havel, Jiri Brno Univ Technol Graph FIT Brno Czech Republic
We are dealing with the problem of fine-grained vehicle make& model recognition and verification. Our contribution is showing that extracting additional data from the video stream - besides the vehicle image itsel... 详细信息
来源: 评论
Quantized Convolutional Neural Networks for Mobile Devices  29
Quantized Convolutional Neural Networks for Mobile Devices
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Wu, Jiaxiang Leng, Cong Wang, Yuhang Hu, Qinghao Cheng, Jian Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models ... 详细信息
来源: 评论
Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face recognition and Pose Estimation  29
Discriminative Invariant Kernel Features: A Bells-and-Whistl...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Pal, Dipan K. Juefei-Xu, Felix Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA
We propose an explicitly discriminative and 'simple' approach to generate invariance to nuisance transformations modeled as unitary. In practice, the approach works well to handle non-unitary transformations a... 详细信息
来源: 评论
Sparse Coding for Third-order Super-symmetric Tensor Descriptors with Application to Texture recognition  29
Sparse Coding for Third-order Super-symmetric Tensor Descrip...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Koniusz, Piotr Cherian, Anoop Natl ICT Australia NICTA Canberra Res Lab Canberra ACT Australia Australian Natl Univ ARC Ctr Excellence Robot Vis Canberra ACT Australia
Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descri... 详细信息
来源: 评论
The MegaFace Benchmark: 1 Million Faces for recognition at Scale  29
The MegaFace Benchmark: 1 Million Faces for Recognition at S...
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Kemelmacher-Shlizerman, Ira Seitz, Steven M. Miller, Daniel Brossard, Evan Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA
Recent face recognition experiments on a major benchmark (LFW [15]) show stunning performance-a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evalua... 详细信息
来源: 评论
Pose-Aware Face recognition in the Wild  29
Pose-Aware Face Recognition in the Wild
收藏 引用
2016 ieee conference on computer vision and pattern recognition (cvpr)
作者: Masi, Iacopo Rawls, Stephen Medioni, Gerard Natarajan, Prem USC Inst Robot & Intelligent Syst Los Angeles CA 90089 USA USC Inst Informat Sci Marina Del Rey CA USA
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations. As opposed to current techniques which either expect a single model to learn... 详细信息
来源: 评论