Yugnaynehc / PaperLegend

The classical paper for computer vision, natural language processing, multimedia and machine learning

Paper Legend

Computer Vision

• Roberts, Lawrence Gilman, ‘Machine Perception of Three-Dimensional Solids’, Doctoral Dissertation, Massachusetts Institute of Technology, 1963 http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0413529

• Horn, Berthold K P, and Brian G. Schunck, ‘Determining Optical Flow’, Artificial Intelligence, 17 (1981), 185–203 https://doi.org/10.1016/0004-3702(81)90024-2

• Lucas, Bruce D, and Takeo Kanade, ‘An Iterative Image Registration Technique with an Application to Stereo Vision.’, in IJCAI, 1981, pp. 674–79 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.2019

• Canny, John F., ‘A Computational Approach to Edge Detection’, PAMI, 8 (1986), 679–98 https://doi.org/10.1109/TPAMI.1986.4767851

• Kass, M., a. Witkin, and D. Terzopoulos, ‘Snakes: Active Contour Models’, IJCV, 1 (1988), 321–31 https://doi.org/10.1007/BF00133570

• Perona, P, and J Malik, ‘Scale-Space and Edge Detection Using Anisotropic Diffusion’, PAMI, 12 (1990), 629–39 https://doi.org/10.1109/34.56205

• Turk, Matthew, and Alex Pentland, ‘Eigenfaces for Recognition’, in CVPR, 1991, iii, 71–86 https://doi.org/10.1162/jocn.1991.3.1.71

• Shi, Jianbo, and Carlo Tomasi, ‘Good Features to Track’, in CVPR, 1994, pp. 593–600 https://doi.org/10.1109/cvpr.1994.323794

• Belhumeur, Peter N., Joäo P. Hespanha, and David J. Kriegman, ‘Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection’, PAMI, 19 (1997), 711–20 https://doi.org/10.1109/34.598228

• Cootes, T.F., G.J. Edwards, and C.J. Taylor, ‘Active Appearance Models’, in ECCV, 1998, ii, 484–98 https://doi.org/10.1109/34.927467

• LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner, ‘Gradient-Based Learning Applied to Document Recognition’, Proceedings of the IEEE, 86 (1998), 2278–2323 https://doi.org/10.1109/5.726791

• Harris, Chris, and Mike Stephens, ‘A Combined Corner and Edge Detector’, in BMVC, 1998, pp. 147–51 https://doi.org/10.5244/C.2.23

• Efros, Alexei, and Thomas Leung, ‘Texture Synthesis by Non-Parametric Sampling’, in ICCV, 1999, ii, 1033–38 https://doi.org/10.1109/ICCV.1999.790383

• Triggs, Bill, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon, ‘Bundle Adjustment — a Modern Synthesis’, VisAlgs, 1999 https://doi.org/10.1007/3-540-44480-7_21

• Shi, Jianbo, and Jitendra Malik, ‘Normalized Cuts and Image Segmentation’, PAMI, 22 (2000), 888–905 https://doi.org/10.1109/34.868688

• Viola, P, and M Jones, ‘Rapid Object Detection Using a Boosted Cascade of Simple Features’, in CVPR, 2001, i, I--511--I--518 https://doi.org/10.1109/CVPR.2001.990517

• Oliva, Aude, and Antonio Torralba, ‘Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope’, IJCV, 42 (2001), 145–75 https://doi.org/10.1023/A:1011139631724

• Boykov, Y., O. Veksler, and R. Zabih, ‘Fast Approximate Energy Minimization via Graph Cuts’, PAMI, 23 (2001), 1222–39 https://doi.org/10.1109/34.969114

• Scharstein, D., R. Szeliski, and R. Zabih, ‘A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms’, in SMBV, 2001, pp. 131–40 https://doi.org/10.1109/SMBV.2001.988771

• Matas, J., O. Chum, M. Urban, and T. Pajdla, ‘Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions’, in BMVC, 2002, pp. 761–67 https://doi.org/10.1016/j.imavis.2004.02.006

• Belongie, Serge, Jitendra Malik, and Jan Puzicha, ‘Shape Matching and Object Recognition Using Shape Contexts’, PAMI, 24 (2002), 509–22 https://doi.org/10.1109/34.993558

• Comaniciu, Dorin, and Peter Meer, ‘Mean Shift: A Robust Approach toward Feature Space Analysis’, PAMI, 24 (2002), 603–19 https://doi.org/10.1109/34.1000236

• Sivic, J., and A. Zisserman, ‘Video Google: A Text Retrieval Approach to Object Matching in Videos’, in ICCV, 2003, pp. 2–9 https://doi.org/10.1109/ICCV.2003.1238663

• Schmid, Cordelia, and Krystian Mikolajczyk, ‘A Performance Evaluation of Local Descriptors’, in ICPR, 2003 https://doi.org/10.1109/TPAMI.2005.188

• Fei-Fei, Li, Rob Fergus, and Pietro Perona, ‘Learning Generative Visual Models from Few Training Examples’, GMBV, 2004 https://doi.org/10.1016/j.cviu.2005.09.012

• Lowe, David G., ‘Distinctive Image Features from Scale-Invariant Keypoints’, IJCV, 60 (2004), 91–110 https://doi.org/10.1023/B:VISI.0000029664.99615.94

• Dalal, Navneet, and Bill Triggs, ‘Histograms of Oriented Gradients for Human Detection’, in CVPR, 2005, I, 886–93 https://doi.org/10.1109/CVPR.2005.177

• Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce, ‘Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories’, in CVPR, 2006, ii, 2169–78 https://doi.org/10.1109/CVPR.2006.68

• Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool, ‘Speeded-up Robust Features (SURF)’, in ECCV, 2006, cx, 346–59 https://doi.org/10.1016/j.cviu.2007.09.014

• Torralba, Antonio, Rob Fergus, and William T. Freeman, ‘80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition’, PAMI, 30 (2008), 1958–70 https://doi.org/10.1109/TPAMI.2008.128

• Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li, ‘ImageNet: A Large-Scale Hierarchical Image Database.’, in CVPR, 2009, pp. 248–55 https://doi.org/10.1109/CVPRW.2009.5206848

• Everingham, Mark, Luc Van Gool, Christopher K.I. Williams, John Winn, and Andrew Zisserman, ‘The Pascal Visual Object Classes (VOC) Challenge’, IJCV, 88 (2010), 303–38 https://doi.org/10.1007/s11263-009-0275-4

• Felzenszwalb, Pedro F, Ross B Girshick, David Mcallester, and Deva Ramanan, ‘Object Detection with Discriminatively Trained Part-Based Models’, PAMI, 2010 https://doi.org/10.1109/TPAMI.2009.167

• Shotton, Jamie, Andrew Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore, and others, ‘Real-Time Human Pose Recognition in Parts from Single Depth Images’, in CVPR, 2011 https://doi.org/10.1109/CVPR.2011.5995316

• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E Hinton, ‘ImageNet Classification with Deep Convolutional Neural Networks’, in NIPS, 2012, pp. 1–9 https://doi.org/10.1145/3065386

• Girshick, Ross, Jeff Donahue, Trevor Darrell, and Jitendra Malik, ‘Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation’, in CVPR, 2014, pp. 580–87 https://doi.org/10.1109/CVPR.2014.81

• Simonyan, Karen, and Andrew Zisserman, ‘Very Deep Convolutional Networks for Large-Scale Image Recognition’, in ICLR, 2015 https://arxiv.org/abs/1409.1556

• Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, and others, ‘ImageNet Large Scale Visual Recognition Challenge’, IJCV, 115 (2015), 211–52 https://doi.org/10.1007/s11263-015-0816-y

• Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, and others, ‘Going Deeper with Convolutions’, in CVPR, 2015 https://doi.org/10.1109/CVPR.2015.7298594

• He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, ‘Deep Residual Learning for Image Recognition’, in CVPR, 2016 https://doi.org/10.1109/CVPR.2016.90

Natural Language Processing

• Mikolov, Tomas, Greg Corrado, Kai Chen, and Jeffrey Dean, ‘Efficient Estimation of Word Representations in Vector Space’, in ICLR, 2013 http://arxiv.org/abs/1301.3781

Machine Learning

• Vapnik, Vladimir Naumovich, The Nature of Statistical Learning Theory, Springer, 1995

• Schapire, Robert E., Yoav Freund, Peter Bartlett, and Wee Sun Lee, ‘Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods’, Annals of Statistics, 26 (1998), 1651–86 https://doi.org/10.1214/AOS/1024691352

• Roweis, Sam T, and Lawrence K Saul, ‘Nonlinear Dimensionality Reduction by Locally Linear Embedding’, Science, 290 (2000), 2323–26 https://doi.org/10.1126/science.290.5500.2323

• Tenenbaum, J B, V de Silva, and J C Langford, ‘A Global Geometric Framework for Nonlinear Dimensionality Reduction.’, Science, 290 (2000), 2319–23 https://doi.org/10.1126/science.290.5500.2319

• Candès, Emmanuel J, Xiaodong Li, Yi Ma, and John Wright, ‘Robust Principal Component Analysis?’, Journal of the ACM, 58 (2011), 1–37 https://doi.org/10.1145/1970392.1970395