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  • 计算机视觉最佳论文

    2018-06-10 21:33:53
    计算机视觉最佳论文,计算机视觉论文,CVPR 计算机视觉
  • 计算机视觉论文

    2018-08-11 09:23:49
    关于计算机视觉的一篇论文,关于计算机视觉的一篇论文
  • 计算机视觉CVPR论文

    2018-06-10 21:31:05
    计算机视觉最佳论文,cvpr,入选最佳论文计算机视觉最佳论文
  • 计算机视觉经典论文集列表 计算机视觉、图像处理、深度学习
  • 计算机视觉经典论文

    2013-11-29 09:40:54
    计算机视觉2006-2012经典论文,是视觉学习的必备论文
  • 计算机视觉_论文

    2015-12-16 23:24:26
    A Deep Convolutional Feature learned by positive sharing Loss for Contour Detection,计算机视觉相关论文,轮廓线检测
  • 计算机视觉论文.zip

    2021-04-01 17:16:16
    计算机视觉课程论文
  • 斯坦福计算机视觉实验室04年至今所有论文,共分五份(资源大小限制),对计算机视觉有兴趣的可以下载学习,可用于了解计算机视觉发展体系
  • 斯坦福计算机视觉实验室04年至今所有论文,共分五份(资源大小限制),对计算机视觉有兴趣的可以下载学习,可用于了解计算机视觉发展体系
  • 斯坦福计算机视觉实验室04年至今所有论文,共分五份(资源大小限制),对计算机视觉有兴趣的可以下载学习,可用于了解计算机视觉发展体系
  • 谷歌最新计算机视觉相关文章,对于搞计算机视觉的人来说是很不错的参考!
  • 计算机视觉相关的论文,详细描述了计算机视觉方向的内容,为计算机视觉方向的研究打下很好的基础。从论文中找到自己的方向。
  • 计算机视觉论文.pdf

    2021-10-10 08:20:41
    计算机视觉论文.pdf
  • 计算机视觉经典论文整理

    千次阅读 2019-08-24 13:25:09
    计算机视觉论文 ImageNet分类 物体检测 物体跟踪 低级视觉 边缘检测 语义分割 视觉注意力和显著性 物体识别 人体姿态估计 CNN原理和性质(Understanding CNN) 图像和语言 图像解说 视频解说 图像生成...

    经典论文

    计算机视觉论文

    1. ImageNet分类
    2. 物体检测
    3. 物体跟踪
    4. 低级视觉
    5. 边缘检测
    6. 语义分割
    7. 视觉注意力和显著性
    8. 物体识别
    9. 人体姿态估计
    10. CNN原理和性质(Understanding CNN)
    11. 图像和语言
    12. 图像解说
    13. 视频解说
    14. 图像生成

    微软ResNet

    论文:用于图像识别的深度残差网络

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1512.03385v1.pdf

    微软PRelu(随机纠正线性单元/权重初始化)

    论文:深入学习整流器:在ImageNet分类上超越人类水平

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1502.01852.pdf

    谷歌Batch Normalization

    论文:批量归一化:通过减少内部协变量来加速深度网络训练

    作者:Sergey Ioffe, Christian Szegedy

    链接:http://arxiv.org/pdf/1502.03167.pdf

    谷歌GoogLeNet

    论文:更深的卷积,CVPR 2015

    作者:Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

    链接:http://arxiv.org/pdf/1409.4842.pdf

    牛津VGG-Net

    论文:大规模视觉识别中的极深卷积网络,ICLR 2015

    作者:Karen Simonyan & Andrew Zisserman

    链接:http://arxiv.org/pdf/1409.1556.pdf

    AlexNet

    论文:使用深度卷积神经网络进行ImageNet分类

    作者:Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    链接:http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    物体检测

    这里写图片描述

    PVANET

    论文:用于实时物体检测的深度轻量神经网络(PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection)

    作者:Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park

    链接:http://arxiv.org/pdf/1608.08021

    纽约大学OverFeat

    论文:使用卷积网络进行识别、定位和检测(OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks),ICLR 2014

    作者:Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun

    链接:http://arxiv.org/pdf/1312.6229.pdf

    伯克利R-CNN

    论文:精确物体检测和语义分割的丰富特征层次结构(Rich feature hierarchies for accurate object detection and semantic segmentation),CVPR 2014

    作者:Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik

    链接:http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf

    微软SPP

    论文:视觉识别深度卷积网络中的空间金字塔池化(Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition),ECCV 2014

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1406.4729.pdf

    微软Fast R-CNN

    论文:Fast R-CNN

    作者:Ross Girshick

    链接:http://arxiv.org/pdf/1504.08083.pdf

    微软Faster R-CNN

    论文:使用RPN走向实时物体检测(Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)

    作者:任少卿、何恺明、Ross Girshick、孙剑

    链接:http://arxiv.org/pdf/1506.01497.pdf

    牛津大学R-CNN minus R

    论文:R-CNN minus R

    作者:Karel Lenc, Andrea Vedaldi

    链接:http://arxiv.org/pdf/1506.06981.pdf

    端到端行人检测

    论文:密集场景中端到端的行人检测(End-to-end People Detection in Crowded Scenes)

    作者:Russell Stewart, Mykhaylo Andriluka

    链接:http://arxiv.org/pdf/1506.04878.pdf

    实时物体检测

    论文:你只看一次:统一实时物体检测(You Only Look Once: Unified, Real-Time Object Detection)

    作者:Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

    链接:http://arxiv.org/pdf/1506.02640.pdf

    Inside-Outside Net

    论文:使用跳跃池化和RNN在场景中检测物体(Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks)

    作者:Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick

    链接:http://arxiv.org/abs/1512.04143.pdf

    微软ResNet

    论文:用于图像识别的深度残差网络

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1512.03385v1.pdf

    R-FCN

    论文:通过区域全卷积网络进行物体识别(R-FCN: Object Detection via Region-based Fully Convolutional Networks)

    作者:代季峰,李益,何恺明,孙剑

    链接:http://arxiv.org/abs/1605.06409

    SSD

    论文:单次多框检测器(SSD: Single Shot MultiBox Detector)

    作者:Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg

    链接:http://arxiv.org/pdf/1512.02325v2.pdf

    速度/精度权衡

    论文:现代卷积物体检测器的速度/精度权衡(Speed/accuracy trade-offs for modern convolutional object detectors)

    作者:Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy

    链接:http://arxiv.org/pdf/1611.10012v1.pdf

    物体跟踪

    • 论文:用卷积神经网络通过学习可区分的显著性地图实现在线跟踪(Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network)

    作者:Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

    地址:arXiv:1502.06796.

    • 论文:DeepTrack:通过视觉跟踪的卷积神经网络学习辨别特征表征(DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking)

    作者:Hanxi Li, Yi Li and Fatih Porikli

    发表: BMVC, 2014.

    • 论文:视觉跟踪中,学习深度紧凑图像表示(Learning a Deep Compact Image Representation for Visual Tracking)

    作者:N Wang, DY Yeung

    发表:NIPS, 2013.

    • 论文:视觉跟踪的分层卷积特征(Hierarchical Convolutional Features for Visual Tracking)

    作者:Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang

    发表: ICCV 2015

    • 论文:完全卷积网络的视觉跟踪(Visual Tracking with fully Convolutional Networks)

    作者:Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu,

    发表:ICCV 2015

    • 论文:学习多域卷积神经网络进行视觉跟踪(Learning Multi-Domain Convolutional Neural Networks for Visual Tracking)

    作者:Hyeonseob Namand Bohyung Han

    对象识别(Object Recognition)

    论文:卷积神经网络弱监督学习(Weakly-supervised learning with convolutional neural networks)

    作者:Maxime Oquab,Leon Bottou,Ivan Laptev,Josef Sivic,CVPR,2015

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf

    FV-CNN

    论文:深度滤波器组用于纹理识别和分割(Deep Filter Banks for Texture Recognition and Segmentation)

    作者:Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf

    人体姿态估计(Human Pose Estimation)

    • 论文:使用 Part Affinity Field的实时多人2D姿态估计(Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields)

    作者:Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, CVPR, 2017.

    • 论文:Deepcut:多人姿态估计的联合子集分割和标签(Deepcut: Joint subset partition and labeling for multi person pose estimation)

    作者:Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, CVPR, 2016.

    • 论文:Convolutional pose machines

    作者:Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh, CVPR, 2016.

    • 论文:人体姿态估计的 Stacked hourglass networks(Stacked hourglass networks for human pose estimation)

    作者:Alejandro Newell, Kaiyu Yang, and Jia Deng, ECCV, 2016.

    • 论文:用于视频中人体姿态估计的Flowing convnets(Flowing convnets for human pose estimation in videos)

    作者:Tomas Pfister, James Charles, and Andrew Zisserman, ICCV, 2015.

    • 论文:卷积网络和人类姿态估计图模型的联合训练(Joint training of a convolutional network and a graphical model for human pose estimation)

    作者:Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler, NIPS, 2014.

    理解CNN

    这里写图片描述

    • 论文:通过测量同变性和等价性来理解图像表示(Understanding image representations by measuring their equivariance and equivalence)

    作者:Karel Lenc, Andrea Vedaldi, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf

    • 论文:深度神经网络容易被愚弄:无法识别的图像的高置信度预测(Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images)

    作者:Anh Nguyen, Jason Yosinski, Jeff Clune, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf

    • 论文:通过反演理解深度图像表示(Understanding Deep Image Representations by Inverting Them)

    作者:Aravindh Mahendran, Andrea Vedaldi, CVPR, 2015

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf

    • 论文:深度场景CNN中的对象检测器(Object Detectors Emerge in Deep Scene CNNs)

    作者:Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR, 2015.

    链接:http://arxiv.org/abs/1412.6856

    • 论文:用卷积网络反演视觉表示(Inverting Visual Representations with Convolutional Networks)

    作者:Alexey Dosovitskiy, Thomas Brox, arXiv, 2015.

    链接:http://arxiv.org/abs/1506.02753

    • 论文:可视化和理解卷积网络(Visualizing and Understanding Convolutional Networks)

    作者:Matthrew Zeiler, Rob Fergus, ECCV, 2014.

    链接:http://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf

    图像与语言

    图像说明(Image Captioning)

    这里写图片描述

    UCLA / Baidu

    用多模型循环神经网络解释图像(Explain Images with Multimodal Recurrent Neural Networks)

    Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, arXiv:1410.1090

    http://arxiv.org/pdf/1410.1090

    Toronto

    使用多模型神经语言模型统一视觉语义嵌入(Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models)

    Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, arXiv:1411.2539.

    http://arxiv.org/pdf/1411.2539

    Berkeley

    用于视觉识别和描述的长期循环卷积网络(Long-term Recurrent Convolutional Networks for Visual Recognition and Description)

    Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, arXiv:1411.4389.

    http://arxiv.org/pdf/1411.4389

    Google

    看图写字:神经图像说明生成器(Show and Tell: A Neural Image Caption Generator)

    Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, arXiv:1411.4555.

    http://arxiv.org/pdf/1411.4555

    Stanford

    用于生成图像描述的深度视觉语义对齐(Deep Visual-Semantic Alignments for Generating Image Description)

    Andrej Karpathy, Li Fei-Fei, CVPR, 2015.

    Web:http://cs.stanford.edu/people/karpathy/deepimagesent/

    Paper:http://cs.stanford.edu/people/karpathy/cvpr2015.pdf

    UML / UT

    使用深度循环神经网络将视频转换为自然语言(Translating Videos to Natural Language Using Deep Recurrent Neural Networks)

    Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, NAACL-HLT, 2015.

    http://arxiv.org/pdf/1412.4729

    CMU / Microsoft

    学习图像说明生成的循环视觉表示(Learning a Recurrent Visual Representation for Image Caption Generation)

    Xinlei Chen, C. Lawrence Zitnick, arXiv:1411.5654.

    Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015

    http://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf

    Microsoft

    从图像说明到视觉概念(From Captions to Visual Concepts and Back)

    Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, CVPR, 2015.

    http://arxiv.org/pdf/1411.4952

    Univ. Montreal / Univ. Toronto

    Show, Attend, and Tell:视觉注意力与神经图像标题生成(Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention)

    Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, arXiv:1502.03044 / ICML 2015

    http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf

    Idiap / EPFL / Facebook

    基于短语的图像说明(Phrase-based Image Captioning)

    Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, arXiv:1502.03671 / ICML 2015

    http://arxiv.org/pdf/1502.03671

    UCLA / Baidu

    像孩子一样学习:从图像句子描述快速学习视觉的新概念(Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images)

    Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, arXiv:1504.06692

    http://arxiv.org/pdf/1504.06692

    MS + Berkeley

    探索图像说明的最近邻方法( Exploring Nearest Neighbor Approaches for Image Captioning)

    Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, arXiv:1505.04467

    http://arxiv.org/pdf/1505.04467.pdf

    图像说明的语言模型(Language Models for Image Captioning: The Quirks and What Works)

    Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, arXiv:1505.01809

    http://arxiv.org/pdf/1505.01809.pdf

    阿德莱德

    具有中间属性层的图像说明( Image Captioning with an Intermediate Attributes Layer)

    Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, arXiv:1506.01144

    蒂尔堡

    通过图片学习语言(Learning language through pictures)

    Grzegorz Chrupala, Akos Kadar, Afra Alishahi, arXiv:1506.03694

    蒙特利尔大学

    使用基于注意力的编码器-解码器网络描述多媒体内容(Describing Multimedia Content using Attention-based Encoder-Decoder Networks)

    Kyunghyun Cho, Aaron Courville, Yoshua Bengio, arXiv:1507.01053

    康奈尔

    图像表示和神经图像说明的新领域(Image Representations and New Domains in Neural Image Captioning)

    Jack Hessel, Nicolas Savva, Michael J. Wilber, arXiv:1508.02091

    MS + City Univ. of HongKong

    Learning Query and Image Similarities with Ranking Canonical Correlation Analysis

    Ting Yao, Tao Mei, and Chong-Wah Ngo, ICCV, 2015

    视频字幕(Video Captioning)

    伯克利

    Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015.

    犹他州/ UML / 伯克利

    Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729.

    微软

    Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.

    犹他州/ UML / 伯克利

    Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence–Video to Text, arXiv:1505.00487.

    蒙特利尔大学/ 舍布鲁克

    Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029

    MPI / 伯克利

    Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698

    多伦多大学 / MIT

    Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724

    蒙特利尔大学

    Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053

    TAU / 美国南加州大学

    Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf, Temporal Tessellation for Video Annotation and Summarization, arXiv:1612.06950.

    图像生成

    卷积/循环网络

    • 论文:Conditional Image Generation with PixelCNN Decoders”

    作者:Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu

    • 论文:Learning to Generate Chairs with Convolutional Neural Networks

    作者:Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

    发表:CVPR, 2015.

    • 论文:DRAW: A Recurrent Neural Network For Image Generation

    作者:Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

    发表:ICML, 2015.

    对抗网络

    • 论文:生成对抗网络(Generative Adversarial Networks)

    作者:Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

    发表:NIPS, 2014.

    • 论文:使用对抗网络Laplacian Pyramid 的深度生成图像模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)

    作者:Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus

    发表:NIPS, 2015.

    • 论文:生成模型演讲概述 (A note on the evaluation of generative models)

    作者:Lucas Theis, Aäron van den Oord, Matthias Bethge

    发表:ICLR 2016.

    • 论文:变分自动编码深度高斯过程(Variationally Auto-Encoded Deep Gaussian Processes)

    作者:Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence

    发表:ICLR 2016.

    • 论文:用注意力机制从字幕生成图像 (Generating Images from Captions with Attention)

    作者:Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov

    发表: ICLR 2016

    • 论文:分类生成对抗网络的无监督和半监督学习(Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks)

    作者:Jost Tobias Springenberg

    发表:ICLR 2016

    • 论文:用一个对抗检测表征(Censoring Representations with an Adversary)

    作者:Harrison Edwards, Amos Storkey

    发表:ICLR 2016

    • 论文:虚拟对抗训练实现分布式顺滑 (Distributional Smoothing with Virtual Adversarial Training)

    作者:Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii

    发表:ICLR 2016

    • 论文:自然图像流形上的生成视觉操作(Generative Visual Manipulation on the Natural Image Manifold)

    作者:朱俊彦, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros

    发表: ECCV 2016.

    • 论文:深度卷积生成对抗网络的无监督表示学习(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)

    作者:Alec Radford, Luke Metz, Soumith Chintala

    发表: ICLR 2016

    问题回答

    这里写图片描述

    弗吉尼亚大学 / 微软研究院

    论文:VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop.

    作者:Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh

    MPI / 伯克利

    论文:Ask Your Neurons: A Neural-based Approach to Answering Questions about Images

    作者:Mateusz Malinowski, Marcus Rohrbach, Mario Fritz,

    发布 : arXiv:1505.01121.

    多伦多

    论文: Image Question Answering: A Visual Semantic Embedding Model and a New Dataset

    作者:Mengye Ren, Ryan Kiros, Richard Zemel

    发表: arXiv:1505.02074 / ICML 2015 deep learning workshop.

    百度/ 加州大学洛杉矶分校

    作者:Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, 徐伟

    论文:Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

    发表: arXiv:1505.05612.

    POSTECH(韩国)

    论文:Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction

    作者:Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han

    发表: arXiv:1511.05765

    CMU / 微软研究院

    论文:Stacked Attention Networks for Image Question Answering

    作者:Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. (2015)

    发表: arXiv:1511.02274.

    MetaMind

    论文:Dynamic Memory Networks for Visual and Textual Question Answering

    作者:Xiong, Caiming, Stephen Merity, and Richard Socher

    发表: arXiv:1603.01417 (2016).

    首尔国立大学 + NAVER

    论文:Multimodal Residual Learning for Visual QA

    作者:Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang

    发表:arXiv:1606:01455

    UC Berkeley + 索尼

    论文:Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

    作者:Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach

    发表:arXiv:1606.01847

    Postech

    论文:Training Recurrent Answering Units with Joint Loss Minimization for VQA

    作者:Hyeonwoo Noh and Bohyung Han

    发表: arXiv:1606.03647

    首尔国立大学 + NAVER

    论文: Hadamard Product for Low-rank Bilinear Pooling

    作者:Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhan

    发表:arXiv:1610.04325.

    视觉注意力和显著性

    这里写图片描述
    论文:Predicting Eye Fixations using Convolutional Neural Networks

    作者:Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu

    发表:CVPR, 2015.

    学习地标的连续搜索

    作者:Learning a Sequential Search for Landmarks

    论文:Saurabh Singh, Derek Hoiem, David Forsyth

    发表:CVPR, 2015.

    视觉注意力机制实现多物体识别

    论文:Multiple Object Recognition with Visual Attention

    作者:Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu,

    发表:ICLR, 2015.

    视觉注意力机制的循环模型

    作者:Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

    论文:Recurrent Models of Visual Attention

    发表:NIPS, 2014.

    低级视觉

    超分辨率

    • Iterative Image Reconstruction

    Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001.

    Sven Behnke: Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid. International Journal of Computational Intelligence and Applications, vol. 1, no. 4, pp. 427-438, 2001.

    • Super-Resolution (SRCNN)

    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.

    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092.

    • Very Deep Super-Resolution

    Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015.

    • Deeply-Recursive Convolutional Network

    Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015.

    • Casade-Sparse-Coding-Network

    Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015.

    • Perceptual Losses for Super-Resolution

    Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016.

    • SRGAN

    Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, arXiv:1609.04802v3, 2016.

    其他应用

    Optical Flow (FlowNet)

    Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.

    Compression Artifacts Reduction

    Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.

    Blur Removal

    Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444

    Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015

    Image Deconvolution

    Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.

    Deep Edge-Aware Filter

    Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.

    Computing the Stereo Matching Cost with a Convolutional Neural Network

    Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.

    Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016

    Feature Learning by Inpainting

    Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros, Context Encoders: Feature Learning by Inpainting, CVPR, 2016

    边缘检测

    这里写图片描述
    Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.

    DeepEdge

    Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.

    DeepContour

    Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.

    语义分割

    这里写图片描述

    SEC: Seed, Expand and Constrain

    Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016.

    Adelaide

    Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. (1st ranked in VOC2012)

    Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. (4th ranked in VOC2012)

    Deep Parsing Network (DPN)

    Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 (2nd ranked in VOC 2012)

    CentraleSuperBoundaries, INRIA

    Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)

    BoxSup

    Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)

    POSTECH

    Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. (7th ranked in VOC2012)

    Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924.

    Seunghoon Hong,Junhyuk Oh,Bohyung Han, andHonglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928

    Conditional Random Fields as Recurrent Neural Networks

    Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)

    DeepLab

    Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. (9th ranked in VOC2012)

    Zoom-out

    Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015

    Joint Calibration

    Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.

    Fully Convolutional Networks for Semantic Segmentation

    Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.

    Hypercolumn

    Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.

    Deep Hierarchical Parsing

    Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015.

    Learning Hierarchical Features for Scene Labeling

    Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.

    Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.

    University of Cambridge

    Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” arXiv preprint arXiv:1511.00561, 2015.

    Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla “Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding.” arXiv preprint arXiv:1511.02680, 2015.

    Princeton

    Fisher Yu, Vladlen Koltun, “Multi-Scale Context Aggregation by Dilated Convolutions”, ICLR 2016

    Univ. of Washington, Allen AI

    Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, “Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing”, ICCV, 2015

    INRIA

    Iasonas Kokkinos, “Pusing the Boundaries of Boundary Detection Using deep Learning”, ICLR 2016

    UCSB

    Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, “Weakly supervised graph based semantic segmentation by learning communities of image-parts”, ICCV, 2015

    其他资源

    课程

    深度视觉

    [斯坦福] CS231n: Convolutional Neural Networks for Visual Recognition

    [香港中文大学] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)

    · 更多深度课程推荐

    [斯坦福] CS224d: Deep Learning for Natural Language Processing

    [牛津 Deep Learning by Prof. Nando de Freitas

    [纽约大学] Deep Learning by Prof. Yann LeCun

    图书

    免费在线图书

    Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    Neural Networks and Deep Learning by Michael Nielsen

    Deep Learning Tutorial by LISA lab, University of Montreal

    视频

    演讲

    Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng

    Recent Developments in Deep Learning By Geoff Hinton

    The Unreasonable Effectiveness of Deep Learning by Yann LeCun

    Deep Learning of Representations by Yoshua bengio

    软件

    框架

    • Tensorflow: An open source software library for numerical computation using data flow graph by Google [Web]
    • Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Web]
    • Torch-based deep learning libraries: [torchnet],
    • Caffe: Deep learning framework by the BVLC [Web]
    • Theano: Mathematical library in Python, maintained by LISA lab [Web]
    • Theano-based deep learning libraries: [Pylearn2], [Blocks], [Keras], [Lasagne]
    • MatConvNet: CNNs for MATLAB [Web]
    • MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [Web]
    • Deepgaze: A computer vision library for human-computer interaction based on CNNs [Web]

    应用

    • 对抗训练 Code and hyperparameters for the paper “Generative Adversarial Networks” [Web]
    • 理解与可视化 Source code for “Understanding Deep Image Representations by Inverting Them,” CVPR, 2015. [Web]
    • 词义分割 Source code for the paper “Rich feature hierarchies for accurate object detection and semantic segmentation,” CVPR, 2014. [Web] ; Source code for the paper “Fully Convolutional Networks for Semantic Segmentation,” CVPR, 2015. [Web]
    • 超分辨率 Image Super-Resolution for Anime-Style-Art [Web]
    • 边缘检测 Source code for the paper “DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection,” CVPR, 2015. [Web]
    • Source code for the paper “Holistically-Nested Edge Detection”, ICCV 2015. [Web]

    讲座

    • [CVPR 2014] Tutorial on Deep Learning in Computer Vision
    • [CVPR 2015] Applied Deep Learning for Computer Vision with Torch

    博客

    • Deep down the rabbit hole: CVPR 2015 and beyond@Tombone’s Computer Vision Blog
    • CVPR recap and where we’re going@Zoya Bylinskii (MIT PhD Student)’s Blog
    • Facebook’s AI Painting@Wired
    • Inceptionism: Going Deeper into Neural Networks@Google Research
    • Implementing Neural networks
    展开全文
  • 计算机视觉论文整理

    万次阅读 多人点赞 2018-05-30 10:19:42
    本文梳理了2012到2017年计算机视觉领域的大事件:以论文和其他干货资源为主,并附上资源地址。囊括上百篇论文,分ImageNet 分类、物体检测、物体追踪、物体识别、图像与语言和图像生成等多个方向进行介绍。 上述的...

    经典论文

    计算机视觉论文

    1. ImageNet分类
    2. 物体检测
    3. 物体跟踪
    4. 低级视觉
    5. 边缘检测
    6. 语义分割
    7. 视觉注意力和显著性
    8. 物体识别
    9. 人体姿态估计
    10. CNN原理和性质(Understanding CNN)
    11. 图像和语言
    12. 图像解说
    13. 视频解说
    14. 图像生成

    微软ResNet

    论文:用于图像识别的深度残差网络

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1512.03385v1.pdf

    微软PRelu(随机纠正线性单元/权重初始化)

    论文:深入学习整流器:在ImageNet分类上超越人类水平

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1502.01852.pdf

    谷歌Batch Normalization

    论文:批量归一化:通过减少内部协变量来加速深度网络训练

    作者:Sergey Ioffe, Christian Szegedy

    链接:http://arxiv.org/pdf/1502.03167.pdf

    谷歌GoogLeNet

    论文:更深的卷积,CVPR 2015

    作者:Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

    链接:http://arxiv.org/pdf/1409.4842.pdf

    牛津VGG-Net

    论文:大规模视觉识别中的极深卷积网络,ICLR 2015

    作者:Karen Simonyan & Andrew Zisserman

    链接:http://arxiv.org/pdf/1409.1556.pdf

    AlexNet

    论文:使用深度卷积神经网络进行ImageNet分类

    作者:Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

    链接:http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    物体检测

    这里写图片描述

    PVANET

    论文:用于实时物体检测的深度轻量神经网络(PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection)

    作者:Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park

    链接:http://arxiv.org/pdf/1608.08021

    纽约大学OverFeat

    论文:使用卷积网络进行识别、定位和检测(OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks),ICLR 2014

    作者:Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun

    链接:http://arxiv.org/pdf/1312.6229.pdf

    伯克利R-CNN

    论文:精确物体检测和语义分割的丰富特征层次结构(Rich feature hierarchies for accurate object detection and semantic segmentation),CVPR 2014

    作者:Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik

    链接:http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf

    微软SPP

    论文:视觉识别深度卷积网络中的空间金字塔池化(Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition),ECCV 2014

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1406.4729.pdf

    微软Fast R-CNN

    论文:Fast R-CNN

    作者:Ross Girshick

    链接:http://arxiv.org/pdf/1504.08083.pdf

    微软Faster R-CNN

    论文:使用RPN走向实时物体检测(Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)

    作者:任少卿、何恺明、Ross Girshick、孙剑

    链接:http://arxiv.org/pdf/1506.01497.pdf

    牛津大学R-CNN minus R

    论文:R-CNN minus R

    作者:Karel Lenc, Andrea Vedaldi

    链接:http://arxiv.org/pdf/1506.06981.pdf

    端到端行人检测

    论文:密集场景中端到端的行人检测(End-to-end People Detection in Crowded Scenes)

    作者:Russell Stewart, Mykhaylo Andriluka

    链接:http://arxiv.org/pdf/1506.04878.pdf

    实时物体检测

    论文:你只看一次:统一实时物体检测(You Only Look Once: Unified, Real-Time Object Detection)

    作者:Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

    链接:http://arxiv.org/pdf/1506.02640.pdf

    Inside-Outside Net

    论文:使用跳跃池化和RNN在场景中检测物体(Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks)

    作者:Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick

    链接:http://arxiv.org/abs/1512.04143.pdf

    微软ResNet

    论文:用于图像识别的深度残差网络

    作者:何恺明、张祥雨、任少卿和孙剑

    链接:http://arxiv.org/pdf/1512.03385v1.pdf

    R-FCN

    论文:通过区域全卷积网络进行物体识别(R-FCN: Object Detection via Region-based Fully Convolutional Networks)

    作者:代季峰,李益,何恺明,孙剑

    链接:http://arxiv.org/abs/1605.06409

    SSD

    论文:单次多框检测器(SSD: Single Shot MultiBox Detector)

    作者:Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg

    链接:http://arxiv.org/pdf/1512.02325v2.pdf

    速度/精度权衡

    论文:现代卷积物体检测器的速度/精度权衡(Speed/accuracy trade-offs for modern convolutional object detectors)

    作者:Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy

    链接:http://arxiv.org/pdf/1611.10012v1.pdf

    物体跟踪

    • 论文:用卷积神经网络通过学习可区分的显著性地图实现在线跟踪(Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network)

    作者:Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

    地址:arXiv:1502.06796.

    • 论文:DeepTrack:通过视觉跟踪的卷积神经网络学习辨别特征表征(DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking)

    作者:Hanxi Li, Yi Li and Fatih Porikli

    发表: BMVC, 2014.

    • 论文:视觉跟踪中,学习深度紧凑图像表示(Learning a Deep Compact Image Representation for Visual Tracking)

    作者:N Wang, DY Yeung

    发表:NIPS, 2013.

    • 论文:视觉跟踪的分层卷积特征(Hierarchical Convolutional Features for Visual Tracking)

    作者:Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang

    发表: ICCV 2015

    • 论文:完全卷积网络的视觉跟踪(Visual Tracking with fully Convolutional Networks)

    作者:Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu,

    发表:ICCV 2015

    • 论文:学习多域卷积神经网络进行视觉跟踪(Learning Multi-Domain Convolutional Neural Networks for Visual Tracking)

    作者:Hyeonseob Namand Bohyung Han

    对象识别(Object Recognition)

    论文:卷积神经网络弱监督学习(Weakly-supervised learning with convolutional neural networks)

    作者:Maxime Oquab,Leon Bottou,Ivan Laptev,Josef Sivic,CVPR,2015

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf

    FV-CNN

    论文:深度滤波器组用于纹理识别和分割(Deep Filter Banks for Texture Recognition and Segmentation)

    作者:Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf

    人体姿态估计(Human Pose Estimation)

    • 论文:使用 Part Affinity Field的实时多人2D姿态估计(Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields)

    作者:Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, CVPR, 2017.

    • 论文:Deepcut:多人姿态估计的联合子集分割和标签(Deepcut: Joint subset partition and labeling for multi person pose estimation)

    作者:Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, CVPR, 2016.

    • 论文:Convolutional pose machines

    作者:Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh, CVPR, 2016.

    • 论文:人体姿态估计的 Stacked hourglass networks(Stacked hourglass networks for human pose estimation)

    作者:Alejandro Newell, Kaiyu Yang, and Jia Deng, ECCV, 2016.

    • 论文:用于视频中人体姿态估计的Flowing convnets(Flowing convnets for human pose estimation in videos)

    作者:Tomas Pfister, James Charles, and Andrew Zisserman, ICCV, 2015.

    • 论文:卷积网络和人类姿态估计图模型的联合训练(Joint training of a convolutional network and a graphical model for human pose estimation)

    作者:Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler, NIPS, 2014.

    理解CNN

    这里写图片描述

    • 论文:通过测量同变性和等价性来理解图像表示(Understanding image representations by measuring their equivariance and equivalence)

    作者:Karel Lenc, Andrea Vedaldi, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf

    • 论文:深度神经网络容易被愚弄:无法识别的图像的高置信度预测(Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images)

    作者:Anh Nguyen, Jason Yosinski, Jeff Clune, CVPR, 2015.

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf

    • 论文:通过反演理解深度图像表示(Understanding Deep Image Representations by Inverting Them)

    作者:Aravindh Mahendran, Andrea Vedaldi, CVPR, 2015

    链接:
    http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf

    • 论文:深度场景CNN中的对象检测器(Object Detectors Emerge in Deep Scene CNNs)

    作者:Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR, 2015.

    链接:http://arxiv.org/abs/1412.6856

    • 论文:用卷积网络反演视觉表示(Inverting Visual Representations with Convolutional Networks)

    作者:Alexey Dosovitskiy, Thomas Brox, arXiv, 2015.

    链接:http://arxiv.org/abs/1506.02753

    • 论文:可视化和理解卷积网络(Visualizing and Understanding Convolutional Networks)

    作者:Matthrew Zeiler, Rob Fergus, ECCV, 2014.

    链接:http://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf

    图像与语言

    图像说明(Image Captioning)

    这里写图片描述

    UCLA / Baidu

    用多模型循环神经网络解释图像(Explain Images with Multimodal Recurrent Neural Networks)

    Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, arXiv:1410.1090

    http://arxiv.org/pdf/1410.1090

    Toronto

    使用多模型神经语言模型统一视觉语义嵌入(Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models)

    Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, arXiv:1411.2539.

    http://arxiv.org/pdf/1411.2539

    Berkeley

    用于视觉识别和描述的长期循环卷积网络(Long-term Recurrent Convolutional Networks for Visual Recognition and Description)

    Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, arXiv:1411.4389.

    http://arxiv.org/pdf/1411.4389

    Google

    看图写字:神经图像说明生成器(Show and Tell: A Neural Image Caption Generator)

    Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, arXiv:1411.4555.

    http://arxiv.org/pdf/1411.4555

    Stanford

    用于生成图像描述的深度视觉语义对齐(Deep Visual-Semantic Alignments for Generating Image Description)

    Andrej Karpathy, Li Fei-Fei, CVPR, 2015.

    Web:http://cs.stanford.edu/people/karpathy/deepimagesent/

    Paper:http://cs.stanford.edu/people/karpathy/cvpr2015.pdf

    UML / UT

    使用深度循环神经网络将视频转换为自然语言(Translating Videos to Natural Language Using Deep Recurrent Neural Networks)

    Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, NAACL-HLT, 2015.

    http://arxiv.org/pdf/1412.4729

    CMU / Microsoft

    学习图像说明生成的循环视觉表示(Learning a Recurrent Visual Representation for Image Caption Generation)

    Xinlei Chen, C. Lawrence Zitnick, arXiv:1411.5654.

    Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015

    http://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf

    Microsoft

    从图像说明到视觉概念(From Captions to Visual Concepts and Back)

    Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, CVPR, 2015.

    http://arxiv.org/pdf/1411.4952

    Univ. Montreal / Univ. Toronto

    Show, Attend, and Tell:视觉注意力与神经图像标题生成(Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention)

    Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, arXiv:1502.03044 / ICML 2015

    http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf

    Idiap / EPFL / Facebook

    基于短语的图像说明(Phrase-based Image Captioning)

    Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, arXiv:1502.03671 / ICML 2015

    http://arxiv.org/pdf/1502.03671

    UCLA / Baidu

    像孩子一样学习:从图像句子描述快速学习视觉的新概念(Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images)

    Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, arXiv:1504.06692

    http://arxiv.org/pdf/1504.06692

    MS + Berkeley

    探索图像说明的最近邻方法( Exploring Nearest Neighbor Approaches for Image Captioning)

    Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, arXiv:1505.04467

    http://arxiv.org/pdf/1505.04467.pdf

    图像说明的语言模型(Language Models for Image Captioning: The Quirks and What Works)

    Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, arXiv:1505.01809

    http://arxiv.org/pdf/1505.01809.pdf

    阿德莱德

    具有中间属性层的图像说明( Image Captioning with an Intermediate Attributes Layer)

    Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, arXiv:1506.01144

    蒂尔堡

    通过图片学习语言(Learning language through pictures)

    Grzegorz Chrupala, Akos Kadar, Afra Alishahi, arXiv:1506.03694

    蒙特利尔大学

    使用基于注意力的编码器-解码器网络描述多媒体内容(Describing Multimedia Content using Attention-based Encoder-Decoder Networks)

    Kyunghyun Cho, Aaron Courville, Yoshua Bengio, arXiv:1507.01053

    康奈尔

    图像表示和神经图像说明的新领域(Image Representations and New Domains in Neural Image Captioning)

    Jack Hessel, Nicolas Savva, Michael J. Wilber, arXiv:1508.02091

    MS + City Univ. of HongKong

    Learning Query and Image Similarities with Ranking Canonical Correlation Analysis

    Ting Yao, Tao Mei, and Chong-Wah Ngo, ICCV, 2015

    视频字幕(Video Captioning)

    伯克利

    Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015.

    犹他州/ UML / 伯克利

    Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729.

    微软

    Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.

    犹他州/ UML / 伯克利

    Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence–Video to Text, arXiv:1505.00487.

    蒙特利尔大学/ 舍布鲁克

    Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029

    MPI / 伯克利

    Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698

    多伦多大学 / MIT

    Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724

    蒙特利尔大学

    Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053

    TAU / 美国南加州大学

    Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf, Temporal Tessellation for Video Annotation and Summarization, arXiv:1612.06950.

    图像生成

    卷积/循环网络
    • 论文:Conditional Image Generation with PixelCNN Decoders”

    作者:Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu

    • 论文:Learning to Generate Chairs with Convolutional Neural Networks

    作者:Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox

    发表:CVPR, 2015.

    • 论文:DRAW: A Recurrent Neural Network For Image Generation

    作者:Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

    发表:ICML, 2015.

    对抗网络
    • 论文:生成对抗网络(Generative Adversarial Networks)

    作者:Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

    发表:NIPS, 2014.

    • 论文:使用对抗网络Laplacian Pyramid 的深度生成图像模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)

    作者:Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus

    发表:NIPS, 2015.

    • 论文:生成模型演讲概述 (A note on the evaluation of generative models)

    作者:Lucas Theis, Aäron van den Oord, Matthias Bethge

    发表:ICLR 2016.

    • 论文:变分自动编码深度高斯过程(Variationally Auto-Encoded Deep Gaussian Processes)

    作者:Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence

    发表:ICLR 2016.

    • 论文:用注意力机制从字幕生成图像 (Generating Images from Captions with Attention)

    作者:Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov

    发表: ICLR 2016

    • 论文:分类生成对抗网络的无监督和半监督学习(Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks)

    作者:Jost Tobias Springenberg

    发表:ICLR 2016

    • 论文:用一个对抗检测表征(Censoring Representations with an Adversary)

    作者:Harrison Edwards, Amos Storkey

    发表:ICLR 2016

    • 论文:虚拟对抗训练实现分布式顺滑 (Distributional Smoothing with Virtual Adversarial Training)

    作者:Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii

    发表:ICLR 2016

    • 论文:自然图像流形上的生成视觉操作(Generative Visual Manipulation on the Natural Image Manifold)

    作者:朱俊彦, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros

    发表: ECCV 2016.

    • 论文:深度卷积生成对抗网络的无监督表示学习(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)

    作者:Alec Radford, Luke Metz, Soumith Chintala

    发表: ICLR 2016

    问题回答

    这里写图片描述

    弗吉尼亚大学 / 微软研究院

    论文:VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop.

    作者:Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh

    MPI / 伯克利

    论文:Ask Your Neurons: A Neural-based Approach to Answering Questions about Images

    作者:Mateusz Malinowski, Marcus Rohrbach, Mario Fritz,

    发布 : arXiv:1505.01121.

    多伦多

    论文: Image Question Answering: A Visual Semantic Embedding Model and a New Dataset

    作者:Mengye Ren, Ryan Kiros, Richard Zemel

    发表: arXiv:1505.02074 / ICML 2015 deep learning workshop.

    百度/ 加州大学洛杉矶分校

    作者:Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, 徐伟

    论文:Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

    发表: arXiv:1505.05612.

    POSTECH(韩国)

    论文:Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction

    作者:Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han

    发表: arXiv:1511.05765

    CMU / 微软研究院

    论文:Stacked Attention Networks for Image Question Answering

    作者:Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. (2015)

    发表: arXiv:1511.02274.

    MetaMind

    论文:Dynamic Memory Networks for Visual and Textual Question Answering

    作者:Xiong, Caiming, Stephen Merity, and Richard Socher

    发表: arXiv:1603.01417 (2016).

    首尔国立大学 + NAVER

    论文:Multimodal Residual Learning for Visual QA

    作者:Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang

    发表:arXiv:1606:01455

    UC Berkeley + 索尼

    论文:Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

    作者:Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach

    发表:arXiv:1606.01847

    Postech

    论文:Training Recurrent Answering Units with Joint Loss Minimization for VQA

    作者:Hyeonwoo Noh and Bohyung Han

    发表: arXiv:1606.03647

    首尔国立大学 + NAVER

    论文: Hadamard Product for Low-rank Bilinear Pooling

    作者:Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhan

    发表:arXiv:1610.04325.

    视觉注意力和显著性

    这里写图片描述
    论文:Predicting Eye Fixations using Convolutional Neural Networks

    作者:Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu

    发表:CVPR, 2015.

    学习地标的连续搜索

    作者:Learning a Sequential Search for Landmarks

    论文:Saurabh Singh, Derek Hoiem, David Forsyth

    发表:CVPR, 2015.

    视觉注意力机制实现多物体识别

    论文:Multiple Object Recognition with Visual Attention

    作者:Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu,

    发表:ICLR, 2015.

    视觉注意力机制的循环模型

    作者:Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

    论文:Recurrent Models of Visual Attention

    发表:NIPS, 2014.

    低级视觉

    超分辨率
    • Iterative Image Reconstruction

    Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001.

    Sven Behnke: Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid. International Journal of Computational Intelligence and Applications, vol. 1, no. 4, pp. 427-438, 2001.

    • Super-Resolution (SRCNN)

    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.

    Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092.

    • Very Deep Super-Resolution

    Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015.

    • Deeply-Recursive Convolutional Network

    Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015.

    • Casade-Sparse-Coding-Network

    Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015.

    • Perceptual Losses for Super-Resolution

    Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016.

    • SRGAN

    Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, arXiv:1609.04802v3, 2016.

    其他应用

    Optical Flow (FlowNet)

    Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.

    Compression Artifacts Reduction

    Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.

    Blur Removal

    Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444

    Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015

    Image Deconvolution

    Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.

    Deep Edge-Aware Filter

    Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.

    Computing the Stereo Matching Cost with a Convolutional Neural Network

    Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.

    Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016

    Feature Learning by Inpainting

    Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros, Context Encoders: Feature Learning by Inpainting, CVPR, 2016

    边缘检测

    这里写图片描述
    Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.

    DeepEdge

    Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.

    DeepContour

    Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.

    语义分割

    这里写图片描述

    SEC: Seed, Expand and Constrain

    Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016.

    Adelaide

    Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. (1st ranked in VOC2012)

    Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. (4th ranked in VOC2012)

    Deep Parsing Network (DPN)

    Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 (2nd ranked in VOC 2012)

    CentraleSuperBoundaries, INRIA

    Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)

    BoxSup

    Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)

    POSTECH

    Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. (7th ranked in VOC2012)

    Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924.

    Seunghoon Hong,Junhyuk Oh,Bohyung Han, andHonglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928

    Conditional Random Fields as Recurrent Neural Networks

    Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)

    DeepLab

    Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. (9th ranked in VOC2012)

    Zoom-out

    Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015

    Joint Calibration

    Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.

    Fully Convolutional Networks for Semantic Segmentation

    Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.

    Hypercolumn

    Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.

    Deep Hierarchical Parsing

    Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015.

    Learning Hierarchical Features for Scene Labeling

    Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.

    Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.

    University of Cambridge

    Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” arXiv preprint arXiv:1511.00561, 2015.

    Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla “Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding.” arXiv preprint arXiv:1511.02680, 2015.

    Princeton

    Fisher Yu, Vladlen Koltun, “Multi-Scale Context Aggregation by Dilated Convolutions”, ICLR 2016

    Univ. of Washington, Allen AI

    Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, “Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing”, ICCV, 2015

    INRIA

    Iasonas Kokkinos, “Pusing the Boundaries of Boundary Detection Using deep Learning”, ICLR 2016

    UCSB

    Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, “Weakly supervised graph based semantic segmentation by learning communities of image-parts”, ICCV, 2015

    其他资源

    课程

    深度视觉

    [斯坦福] CS231n: Convolutional Neural Networks for Visual Recognition

    [香港中文大学] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)

    · 更多深度课程推荐

    [斯坦福] CS224d: Deep Learning for Natural Language Processing

    [牛津 Deep Learning by Prof. Nando de Freitas

    [纽约大学] Deep Learning by Prof. Yann LeCun

    图书

    免费在线图书

    Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    Neural Networks and Deep Learning by Michael Nielsen

    Deep Learning Tutorial by LISA lab, University of Montreal

    视频

    演讲

    Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng

    Recent Developments in Deep Learning By Geoff Hinton

    The Unreasonable Effectiveness of Deep Learning by Yann LeCun

    Deep Learning of Representations by Yoshua bengio

    软件

    框架
    • Tensorflow: An open source software library for numerical computation using data flow graph by Google [Web]
    • Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Web]
    • Torch-based deep learning libraries: [torchnet],
    • Caffe: Deep learning framework by the BVLC [Web]
    • Theano: Mathematical library in Python, maintained by LISA lab [Web]
    • Theano-based deep learning libraries: [Pylearn2], [Blocks], [Keras], [Lasagne]
    • MatConvNet: CNNs for MATLAB [Web]
    • MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [Web]
    • Deepgaze: A computer vision library for human-computer interaction based on CNNs [Web]

    应用

    • 对抗训练 Code and hyperparameters for the paper “Generative Adversarial Networks” [Web]
    • 理解与可视化 Source code for “Understanding Deep Image Representations by Inverting Them,” CVPR, 2015. [Web]
    • 词义分割 Source code for the paper “Rich feature hierarchies for accurate object detection and semantic segmentation,” CVPR, 2014. [Web] ; Source code for the paper “Fully Convolutional Networks for Semantic Segmentation,” CVPR, 2015. [Web]
    • 超分辨率 Image Super-Resolution for Anime-Style-Art [Web]
    • 边缘检测 Source code for the paper “DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection,” CVPR, 2015. [Web]
    • Source code for the paper “Holistically-Nested Edge Detection”, ICCV 2015. [Web]

    讲座

    • [CVPR 2014] Tutorial on Deep Learning in Computer Vision
    • [CVPR 2015] Applied Deep Learning for Computer Vision with Torch

    博客

    • Deep down the rabbit hole: CVPR 2015 and beyond@Tombone’s Computer Vision Blog
    • CVPR recap and where we’re going@Zoya Bylinskii (MIT PhD Student)’s Blog
    • Facebook’s AI Painting@Wired
    • Inceptionism: Going Deeper into Neural Networks@Google Research
    • Implementing Neural networks
    展开全文
  • 自己整理的IJCAI2018计算机视觉方向的论文,总共90篇,有感兴趣的可以一阅。
  • 最佳计算机视觉论文,全球最佳论文,CVPR ,全球最佳论文
  • 斯坦福计算机视觉实验室04年至今所有论文,共分五份(资源大小限制),对计算机视觉有兴趣的可以下载学习,可用于了解计算机视觉发展体系
  • 2019年计算机视觉综述论文汇总

    千次阅读 2019-12-14 22:36:20
    【导读】本文整理了2019年计算机视觉方面的综述论文,包含目标检测、图像分割(含语义/实例分割)、目标跟踪、医学图像分割、显著性目标检测、行为识别、深度估计等。可以使读者对相关 目标检测 2019 四大目标检测...

    【导读】本文整理了2019年计算机视觉方面的综述论文,包含目标检测、图像分割(含语义/实例分割)、目标跟踪、医学图像分割、显著性目标检测、行为识别、深度估计等。可以使读者对相关

    目标检测

    2019 四大目标检测综述论文:

    Imbalance Problems in Object Detection: A Review

    Recent Advances in Deep Learning for Object Detection

    A Survey of Deep Learning-based Object Detection

    Object Detection in 20 Years: A Survey

    目标检测更多论文详见:【资源】最全目标检测论文汇总(含最新 2019)
     

    图像分割

    Deep Semantic Segmentation of Natural and Medical Images: A Review

    Deep Learning Techniques for Image Segmentation

    • intro: 本综述介绍了从2013年到2019年,主流的30多种分割算法(含语义/实例分割),50多种数据集,共计224篇参考文献
    • 链接: https://arxiv.org/abs/1907.06119

     

    目标跟踪

    A Review of Visual Trackers and Analysis of its Application to Mobile Robot

    Deep Learning in Video Multi-Object Tracking: A Survey

     

    超分辨率

    A Deep Journey into Super-resolution: A survey
    链接: https://arxiv.org/abs/1904.07523Deep
    Learning for Image Super-resolution: A Survey

     

    医学图像分割

    Deep learning for cardiac image segmentation: A review

    • intro: 本医学图像分割综述从FCN(2014)到Dense U-net(2019),超过250篇的参考文献(论文中光画图的工作量就超级大)
    • 链接: https://arxiv.org/abs/1911.03723

    Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

     

    自动驾驶

    A Survey of Autonomous Driving: Common Practices and Emerging Technologies

    • intro: 在本次自动驾驶系统调查中,本文概述了一些关键创新以及现有系统。涉及内容如下: 1. 前景和挑战 2. 自动驾驶系统架构 3. 传感器 4. 定位和建图技术 5. 感知(2D/3D):目标检测、跟踪、道路和车道线检测 6. 评估方法 7. 自动驾驶相关数据集 8. 自动驾驶开源工具
    • 链接: https://arxiv.org/abs/1906.05113

     

    显著性目标检测

    Salient Object Detection in the Deep Learning Era: An In-Depth Survey

    Action Recognition: A Survey

     

    深度估计

    Monocular Depth Estimation: A Survey

    来源:知乎
    作者:Amusi

     

    展开全文
  • 2016年欧洲计算机视觉会议部分论文,值得学习了解目前计算机视觉方面的发展情况
  • 简历 计算机视觉论文论文只要苟且的住,日子肯定过得去每天就看一点点,工作好找一丢丢
  • 来自北京大学的施柏鑫教授为大家讲解计算机视觉会议论文从投稿到接收的整个流程,十分详细具体,感兴趣的同学可以看一下。
  • Markov随机场模型用于低层计算机视觉的研究   目 录 文摘 英文文摘 第一章序言 §1.1计算机视觉的研究进展 §1.2课题背景 §1.3本课题的研究现状 §1.4本文的主要研究工作 §1.5本文的结构安排 第二章基于MRF...
  • 时尚是我们向世界展示自己的方式,已经成为世界上最大的产业之一。时尚主要通过视觉来传达,因此近年来受到了计算机视觉研究者的广泛关注。

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