【专知荟萃19】图像识别Image Recognition知识资料全集(入门/进阶/论文/综述/视频/专家,附查看)

点击上方“专知”关注获取专业AI知识!

【导读】主题荟萃知识是专知的核心功能之一,为用户提供AI领域系统性的知识学习服务。主题荟萃为用户提供全网关于该主题的精华(Awesome)知识资料收录整理,使得AI从业者便捷学习和解决工作问题!在专知人工智能主题知识树基础上,主题荟萃由专业人工编辑和算法工具辅助协作完成,并保持动态更新!另外欢迎对此创作主题荟萃感兴趣的同学,请加入我们专知AI创作者计划,共创共赢! 今天专知为大家呈送第十八篇专知主题荟萃-图像识别知识资料大全集荟萃 (入门/进阶/综述/视频/代码/专家等),请大家查看!专知访问www.zhuanzhi.ai,  或关注微信公众号后台回复" 专知"进入专知,搜索主题“图像识别”查看。此外,我们也提供该文网页桌面手机端(www.zhuanzhi.ai)完整访问,可直接点击访问收录链接地址,以及pdf版下载链接,请文章末尾查看!此为初始版本,请大家指正补充,欢迎在后台留言!欢迎大家分享转发~


  • 图像识别 Image Recognition 专知荟萃

    • 入门学习

    • 进阶文章

    • Imagenet result

    • 2013

    • 2014

    • 2015

    • 2016

    • 2017

    • 综述

    • Tutorial

    • 视频教程

    • Datasets

    • 代码

    • 领域专家


入门学习

  1. 如何识别图像边缘?  阮一峰

    • [http://www.ruanyifeng.com/blog/2016/07/edge-recognition.html]

  2. CS231n课程笔记翻译:图像分类笔记

    • [https://zhuanlan.zhihu.com/p/20894041]

    • [http://cs231n.github.io/classification/]

  3. 深度学习、图像分类入门,从VGG16卷积神经网络开始 [http://blog.csdn.net/Errors_In_Life/article/details/65950699\]

  4.  The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) 翻译

    • [http://blog.csdn.net/darkprince120/article/details/53024714]

  5. 深度学习框架Caffe图片分类教程

    • [http://blog.csdn.net/qq_31258245/article/details/75093380\]

  6. MobileNet教程:用TensorFlow搭建在手机上运行的图像分类器

    • [https://zhuanlan.zhihu.com/p/28199892]

  7. 图像验证码和大规模图像识别技术

    • [http://www.infoq.com/cn/articles/CAPTCHA-image-recognition]

  8. 卷积神经网络如何进行图像识别

    • [http://www.infoq.com/cn/articles/convolutional-neural-networks-image-recognition]

  9. 图像识别与验证码

    • [https://zhuanlan.zhihu.com/securityCode]

  10. 图像识别(知乎话题) - [https://www.zhihu.com/topic/19588774/top-answers?page=1]


进阶文章

Imagenet result

  1. Microsoft (Deep Residual Learning] [http://arxiv.org/pdf/1512.03385v1.pdfSlide](http://image-net.org/challenges/talks/ilsvrc2015_deep_residual_learning_kaiminghe.pdf]][[] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385.

  2. Microsoft (PReLu/Weight Initialization] [http://arxiv.org/pdf/1502.01852] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852.

  3. Batch Normalization [http://arxiv.org/pdf/1502.03167] Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.

  4. GoogLeNet [http://arxiv.org/pdf/1409.4842] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015.

  5. VGG-Net [http://www.robots.ox.ac.uk/~vgg/research/very_deep/] [http://arxiv.org/pdf/1409.1556] Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015.

  6. AlexNet [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.


2013

  1. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell

    • [http://arxiv.org/abs/1310.1531]


2014

  1. CNN Features off-the-shelf: an Astounding Baseline for Recognition CVPR 2014

    • [http://arxiv.org/abs/1403.6382]

  2. Deeply learned face representations are sparse, selective, and robust

    • [http://arxiv.org/abs/1412.1265]

  3. Deep Learning Face Representation by Joint Identification-Verification
    - [https://arxiv.org/abs/1406.4773]

  4. Deep Learning Face Representation from Predicting 10,000 Classes. intro: CVPR 2014

    • [http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf]

  5. Multiple Object Recognition with Visual Attention**

    • [https://arxiv.org/abs/1412.7755]


2015

  1. HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification intro: ICCV 2015

    • [https://arxiv.org/abs/1410.0736]

  2. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. ImageNet top-5 error: 4.94%

    • [http://arxiv.org/abs/1502.01852]

  3. Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios

    • [http://ieeexplore.ieee.org/document/7486476/]

  4. FaceNet: A Unified Embedding for Face Recognition and Clustering

    • [http://arxiv.org/abs/1503.03832]


2016

  1. Humans and deep networks largely agree on which kinds of variation make object recognition harder**

    • [http://arxiv.org/abs/1604.06486]

  2. FusionNet: 3D Object Classification Using Multiple Data Representations

    • [https://arxiv.org/abs/1607.05695]

  3. Deep FisherNet for Object Classification**

    • [http://arxiv.org/abs/1608.00182]

  4. Factorized Bilinear Models for Image Recognition**

    • [https://arxiv.org/abs/1611.05709]

  5. Hyperspectral CNN Classification with Limited Training Samples**

    • [https://arxiv.org/abs/1611.09007]

  6. The More You Know: Using Knowledge Graphs for Image Classification**

    • [https://arxiv.org/abs/1612.04844]

  7. MaxMin Convolutional Neural Networks for Image Classification**

    • [http://webia.lip6.fr/~thomen/papers/Blot_ICIP_2016.pdf]

  8. Cost-Effective Active Learning for Deep Image Classification. TCSVT 2016.

    • [https://arxiv.org/abs/1701.03551]

  9. DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

    • [http://arxiv.org/abs/1606.05675]


2017

  1. Deep Collaborative Learning for Visual Recognition

    • [https://www.arxiv.org/abs/1703.01229]

  2. Bilinear CNN Models for Fine-grained Visual Recognition

    • [http://vis-www.cs.umass.edu/bcnn/]

  3. Multiple Instance Learning Convolutional Neural Networks for Object Recognition**

    • [https://arxiv.org/abs/1610.03155]

  4. B-CNN: Branch Convolutional Neural Network for Hierarchical Classification

    • [https://arxiv.org/abs/1709.09890](

  5. Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification

    • [https://arxiv.org/abs/1709.03439]

  6. Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

    • [https://arxiv.org/abs/1706.07901]
      Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

    • [https://arxiv.org/abs/1707.06335]

  7. Convolutional Low-Resolution Fine-Grained Classification

    • [https://arxiv.org/abs/1703.05393]


综述

  1. A Review of Image Recognition with Deep Convolutional Neural Network

    • [https://link.springer.com/chapter/10.1007/978-3-319-63309-1_7\]

  2. Review on Image Recognition

    • [http://pnrsolution.org/Datacenter/Vol3/Issue2/186.pdf]

  3. 深度学习在图像识别中的研究进展与展望

    • [https://piazza-resources.s3.amazonaws.com/i48o74a0lqu0/i4fcg2o44k63n6/deep_recognition.pdf?AWSAccessKeyId=AKIAIEDNRLJ4AZKBW6HA&Expires=1509460321&Signature=DxZ8LrEEStKQrKESDufA7i3qIGA%3D\]

  4. 图像物体分类与检测算法综述 黄凯奇 任伟强 谭铁牛 [http://cjc.ict.ac.cn/online/cre/hkq-2014526115913.pdf]

  5. Book Chapter - Objecter Recognition

    • [http://www.cse.usf.edu/~r1k/MachineVisionBook/MachineVision.files/MachineVision_Chapter15.pdf\]


Tutorial

  1. CVPR tutorial : Large-Scale Visual Recognition

    • [http://www.europe.naverlabs.com/Research/Computer-Vision/Highlights/CVPR-tutorial-Large-Scale-Visual-Recognition]

  2. Image Recognition with Tensorflow

    • [https://www.tensorflow.org/tutorials/image_recognition\]

  3. Visual Object Recognition Tutorial by Bastian Leibe & Kristen Grauman

    • [https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=32&cad=rja&uact=8&ved=0ahUKEwiWrq3W5JrXAhWFLpQKHQPuCcI4HhAWCC8wAQ&url=http%3A%2F%2Fz.cs.utexas.edu%2Fusers%2Fpiyushk%2Fcourses%2Fspr12%2Fslides%2FAAAI-tutorial-2.ppt&usg=AOvVaw3tQkyK0zW7nZ28LhrGzCUC]


视频教程

  1. CS231n: Convolutional Neural Networks for Visual Recognition

    • [http://cs231n.stanford.edu/]

  2. 李飞飞: 我们怎么教计算机理解图片?
    - [https://www.youtube.com/watch?v=40riCqvRoMs]



Datasets

  1. MNIST: handwritten digits (http://yann.lecun.com/exdb/mnist/)

  2. NIST: similar to MNIST, but larger

  3. Perturbed NIST: a dataset developed in Yoshua’s class (NIST with tons of deformations)

  4. CIFAR10 / CIFAR100: 32×32 natural image dataset with 10/100 categories ( http://www.cs.utoronto.ca/~kriz/cifar.html)

  5. Caltech 101: pictures of objects belonging to 101 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech101/)

  6. Caltech 256: pictures of objects belonging to 256 categories (http://www.vision.caltech.edu/Image_Datasets/Caltech256/) 

  7. Caltech Silhouettes: 28×28 binary images contains silhouettes of the Caltech 101 dataset

  8. STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. http://www.stanford.edu/~acoates//stl10/

  9. The Street View House Numbers (SVHN) Dataset – http://ufldl.stanford.edu/housenumbers/

  10. NORB: binocular images of toy figurines under various illumination and pose (http://www.cs.nyu.edu/~ylclab/data/norb-v1.0/)

  11. Imagenet: image database organized according to the WordNethierarchy (http://www.image-net.org/)

  12. Pascal VOC: various object recognition challenges (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)

  13. Labelme: A large dataset of annotated images, http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

  14. COIL 20: different objects imaged at every angle in a 360 rotation(http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php)

  15. COIL100: different objects imaged at every angle in a 360 rotation (http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php)


代码

  1. AlexNet 

    • [https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet\]

  2. ZFnet [https://github.com/rainer85ah/Papers2Code/tree/master/ZFNet]

  3. VGG

    • [https://github.com/machrisaa/tensorflow-vgg]

  4. GoogLeNet [https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet\]

  5. ResNet

    • [https://github.com/KaimingHe/deep-residual-networks]

  6. HD-CNN

    • [https://sites.google.com/site/homepagezhichengyan/home/hdcnn/code]

  7. Factorized Bilinear Models for Image Recognition

    • [https://github.com/lyttonhao/Factorized-Bilinear-Network]

  8. MaxMin Convolutional Neural Networks for Image Classification

    • [https://github.com/karandesai-96/maxmin-cnn]

  9. Multiple Object Recognition with Visual Attention

    • [https://github.com/jrbtaylor/visual-attention]

  10. Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification

    • [https://github.com/zhufengx/SRN_multilabel/\]

  11. Deep Learning Face Representation from Predicting 10,000 Classes

    • [https://github.com/stdcoutzyx/DeepID_FaceClassify\]

  12. FaceNet: A Unified Embedding for Face Recognition and Clustering

    • [https://github.com/davidsandberg/facenet]

  13. DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

    • [https://github.com/deercoder/DeepFood]


领域专家

  1. Yangqing Jia

    • [http://daggerfs.com/]

  2. Ross Girshick

    • [http://www.rossgirshick.info/]

  3. Xiaodi Hou

    • [http://www.houxiaodi.com/]

  4. Kaiming He

    • [http://kaiminghe.com/]

  5. Jian Sun

    • [http://www.jiansun.org/]

  6. Xiaoou Tang

    • [https://www.ie.cuhk.edu.hk/people/xotang.shtml]

  7. Shuicheng Yan

    • [https://www.ece.nus.edu.sg/stfpage/eleyans/]


初步版本,水平有限,有错误或者不完善的地方,欢迎大家提建议和补充(到专知网站www.zhuanzhi.ai 主题下评论),会一直保持更新,敬请关注http://www.zhuanzhi.ai 和关注专知公众号,获取最新AI相关知识。


欢迎转发分享专业AI知识!


特别提示-专知目标跟踪主题:

请PC登录www.zhuanzhi.ai或者点击阅读原文注册登录,顶端搜索“目标跟踪” 主题,查看评论获得专知荟萃全集知识等资料,直接PC端访问体验更佳!如下图所示~


此外,请关注专知公众号(扫一扫最下面专知二维码,或者点击上方蓝色专知),

  • 后台回复“图像识别”或者“Image” 就可以在手机端获取专知图像识别资料查看链接地址,直接打开荟萃资料的链接地址~~


请扫描专知小助手,加入专知人工智能群交流~

往期专知荟萃知识资料全集获取(关注本公众号-专知,获取下载链接),请查看:

【专知荟萃01】深度学习知识资料大全集(入门/进阶/论文/代码/数据/综述/领域专家等)(附pdf下载)

【专知荟萃02】自然语言处理NLP知识资料大全集(入门/进阶/论文/Toolkit/数据/综述/专家等)(附pdf下载)

【专知荟萃03】知识图谱KG知识资料全集(入门/进阶/论文/代码/数据/综述/专家等)(附pdf下载)

【专知荟萃04】自动问答QA知识资料全集(入门/进阶/论文/代码/数据/综述/专家等)(附pdf下载)

【专知荟萃05】聊天机器人Chatbot知识资料全集(入门/进阶/论文/软件/数据/专家等)(附pdf下载)

【专知荟萃06】计算机视觉CV知识资料大全集(入门/进阶/论文/课程/会议/专家等)(附pdf下载)

【专知荟萃07】自动文摘AS知识资料全集(入门/进阶/代码/数据/专家等)(附pdf下载)

【专知荟萃08】图像描述生成Image Caption知识资料全集(入门/进阶/论文/综述/视频/专家等)

【专知荟萃09】目标检测知识资料全集(入门/进阶/论文/综述/视频/代码等)

【专知荟萃10】推荐系统RS知识资料全集(入门/进阶/论文/综述/视频/代码等)

【专知荟萃11】GAN生成式对抗网络知识资料全集(理论/报告/教程/综述/代码等)

【专知荟萃12】信息检索 Information Retrieval 知识资料全集(入门/进阶/综述/代码/专家,附PDF下载)

【专知荟萃13】工业学术界用户画像 User Profile 实用知识资料全集(入门/进阶/竞赛/论文/PPT,附PDF下载)

【专知荟萃14】机器翻译 Machine Translation知识资料全集(入门/进阶/综述/视频/代码/专家,附PDF下载)

【专知荟萃15】图像检索Image Retrieval知识资料全集(入门/进阶/综述/视频/代码/专家,附PDF下载)

【专知荟萃16】主题模型Topic Model知识资料全集(基础/进阶/论文/综述/代码/专家,附PDF下载)

【专知荟萃17】情感分析Sentiment Analysis 知识资料全集(入门/进阶/论文/综述/视频/专家,附查看)

【专知荟萃18】目标跟踪Object Tracking知识资料全集(入门/进阶/论文/综述/视频/专家,附查看)

-END-

欢迎使用专知

专知,一个新的认知方式!专注在人工智能领域为AI从业者提供专业可信的知识分发服务, 包括主题定制、主题链路、搜索发现等服务,帮你又好又快找到所需知识。


使用方法>>访问www.zhuanzhi.ai, 或点击文章下方“阅读原文”即可访问专知

中国科学院自动化研究所专知团队

@2017 专知

专 · 知

关注我们的公众号,获取最新关于专知以及人工智能的资讯、技术、算法、深度干货等内容。扫一扫下方关注我们的微信公众号。


点击“阅读原文”,使用专知


展开全文
Top
微信扫码咨询专知VIP会员