update pascal leaderboard

README.md

@@ -41,7 +41,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   - [Applications](#applications)
  - [Tutorials](#tutorials)
  - [Blogs](#blogs)
- 
+
 ## Papers
 
 ### ImageNet Classification
@@ -53,7 +53,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Batch Normalization [[Paper]](http://arxiv.org/pdf/1502.03167)
     * Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.
   * GoogLeNet [[Paper]](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. 
+    * Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015.
   * VGG-Net [[Web]](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) [[Paper]](http://arxiv.org/pdf/1409.1556)
    * Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015.
   * AlexNet [[Paper]](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf)
@@ -83,7 +83,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014. [[Paper]](http://www.bmva.org/bmvc/2014/files/paper028.pdf)
  * N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. [[Paper]](http://winsty.net/papers/dlt.pdf)
  * Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, "Hierarchical Convolutional Features for Visual Tracking", ICCV 2015 [[GitHub]](https://github.com/jbhuang0604/CF2)
- 
+
 ### Super-Resolution
  * Super-Resolution (SRCNN) [[Web]](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html) [[Paper-ECCV14]](http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf) [[Paper-arXiv15]](http://arxiv.org/pdf/1501.00092.pdf)
     * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.
@@ -91,7 +91,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * 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. [[Paper]](http://arxiv.org/abs/1511.04587)
  * Deeply-Recursive Convolutional Network
-  * Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [[Paper]](http://arxiv.org/abs/1511.04491) 
+  * Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [[Paper]](http://arxiv.org/abs/1511.04491)
  * Others
     * Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014. [[Paper ICONIP-2014]](http://www.brml.org/uploads/tx_sibibtex/281.pdf)
 
@@ -102,7 +102,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * Compression Artifacts Reduction [[Paper-arXiv15]](http://arxiv.org/pdf/1504.06993)
    * Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
  * Non-Uniform Motion Blur Removal [[Paper]](http://arxiv.org/pdf/1503.00593)
-  * Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015. 
+  * Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015.
  * Image Deconvolution [[Web]](http://lxu.me/projects/dcnn/) [[Paper]](http://lxu.me/mypapers/dcnn_nips14.pdf)
   *  Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
  *  Deep Edge-Aware Filter [[Paper]](http://jmlr.org/proceedings/papers/v37/xub15.pdf)
@@ -115,7 +115,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 (from Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.)
 
  * Holistically-Nested Edge Detection [[Paper]](http://arxiv.org/pdf/1504.06375)
-  * Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375. 
+  * Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.
  * DeepEdge [[Paper]](http://arxiv.org/pdf/1412.1123)
   * Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.
  * DeepContour [[Paper]](http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf)
@@ -124,35 +124,39 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ### Semantic Segmentation
 ![semantic_segmantation](https://cloud.githubusercontent.com/assets/5226447/8452076/0ba8340c-2023-11e5-88bc-bebf4509b6bb.PNG)
 (from Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640.)
-  * PASCAL VOC2012 Challenge Top 10 (14 Aug. 2015)
-![VOC2012_top_10](http://cv.snu.ac.kr/hmyeong/files/150814_pascal_voc.png)
+  * PASCAL VOC2012 Challenge Leaderboard (02 Dec. 2015)
+![VOC2012_top_rankings](https://cloud.githubusercontent.com/assets/7778428/11527440/5724d2bc-9924-11e5-9614-01b863629af3.png)
 (from PASCAL VOC2012 [leaderboards](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6))
   * Adelaide
    * Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [[Paper]](http://arxiv.org/pdf/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. [[Paper]](http://arxiv.org/pdf/1506.02108) (4th ranked in VOC2012)
+  * Deep Parsing Network
+   * Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [[Paper]](http://arxiv.org/pdf/1509.02634.pdf) (2nd ranked in VOC 2012)
+  * CentraleSuperBoundaries, INRIA [[Paper]](http://arxiv.org/pdf/1511.07386)
+   * Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
   * BoxSup [[Paper]](http://arxiv.org/pdf/1503.01640)
-   * Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (2nd ranked in VOC2012)
-  * Conditional Random Fields as Recurrent Neural Networks [[Paper]](http://arxiv.org/pdf/1502.03240)
-   * 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. (3rd 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. [[Paper]](http://arxiv.org/pdf/1502.02734) (5th ranked in VOC2012)
+   * 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. [[Paper]](http://arxiv.org/pdf/1505.04366) (6th ranked in VOC2012)
+   * Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [[Paper]](http://arxiv.org/pdf/1505.04366) (7th ranked in VOC2012)
    * Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [[Paper]](http://arxiv.org/pdf/1506.04924)
+  * Conditional Random Fields as Recurrent Neural Networks [[Paper]](http://arxiv.org/pdf/1502.03240)
+   * 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. [[Paper]](http://arxiv.org/pdf/1502.02734) (9th ranked in VOC2012)
+  * Zoom-out [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf)
+   * Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
   * Joint Calibration [[Paper]](http://arxiv.org/pdf/1507.01581)
    * Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.
   * Fully Convolutional Networks for Semantic Segmentation [[Paper-CVPR15]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) [[Paper-arXiv15]](http://arxiv.org/pdf/1411.4038)
    * Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
   * Hypercolumn [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf)
-   * Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015. 
-  * Zoom-out [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf)
-   * Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015.
+   * 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. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf)
   * Learning Hierarchical Features for Scene Labeling [[Paper-ICML12]](http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf) [[Paper-PAMI13]](http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf)
    * 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 [[Web]](http://mi.eng.cam.ac.uk/projects/segnet/) 
+  * University of Cambridge [[Web]](http://mi.eng.cam.ac.uk/projects/segnet/)
    * Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015. [[Paper]](http://arxiv.org/abs/1511.00561)
    * 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. [[Paper]](http://arxiv.org/abs/1511.00561)
 
@@ -168,13 +172,13 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
    * Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.
   * Recurrent Models of Visual Attention [[Paper]](http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf)
    * Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.
-   
+
 ### Object Recognition
   * Weakly-supervised learning with convolutional neural networks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf)
    * Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
   * FV-CNN [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf)
    * Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.
-   
+
 ### Understanding CNN
 ![understanding](https://cloud.githubusercontent.com/assets/5226447/8452083/1aaa0066-2023-11e5-800b-2248ead51584.PNG)
 (from Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.)
@@ -192,7 +196,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Visualizing and Understanding CNN
    * Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014. [[Paper]](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)
 
-### Image Captioning 
+### Image Captioning
 ![image_captioning](https://cloud.githubusercontent.com/assets/5226447/8452051/e8f81030-2022-11e5-85db-c68e7d8251ce.PNG)
 (from Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.)
 
@@ -207,12 +211,12 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
    * Stanford [[Web]](http://cs.stanford.edu/people/karpathy/deepimagesent/) [[Paper]](http://cs.stanford.edu/people/karpathy/cvpr2015.pdf)
       * Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.
    * UML / UT [[Paper]](http://arxiv.org/pdf/1412.4729)
-      * Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 2015. 
+      * Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 2015.
    * CMU / Microsoft [[Paper-arXiv]](http://arxiv.org/pdf/1411.5654) [[Paper-CVPR]](http://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf)
       * Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generation, arXiv:1411.5654.
       * Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015
    * Microsoft [[Paper]](http://arxiv.org/pdf/1411.4952)
-      * 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, From Captions to Visual Concepts and Back, CVPR, 2015. 
+      * 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, From Captions to Visual Concepts and Back, CVPR, 2015.
    * Univ. Montreal / Univ. Toronto [[Web](http://kelvinxu.github.io/projects/capgen.html)] [[Paper](http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf)]
       * Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, arXiv:1502.03044 / ICML 2015
    * Idiap / EPFL / Facebook [[Paper](http://arxiv.org/pdf/1502.03671)]
@@ -229,7 +233,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
    * Univ. Montreal [[Paper](http://arxiv.org/pdf/1507.01053.pdf)]
       * Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
    * Cornell [[Paper](http://arxiv.org/pdf/1508.02091.pdf)]
-      * Jack Hessel, Nicolas Savva, Michael J. Wilber, Image Representations and New Domains in Neural Image Captioning, arXiv:1508.02091 
+      * Jack Hessel, Nicolas Savva, Michael J. Wilber, Image Representations and New Domains in Neural Image Captioning, arXiv:1508.02091
 
 ### Video Captioning
 * Berkeley [[Web]](http://jeffdonahue.com/lrcn/) [[Paper]](http://arxiv.org/pdf/1411.4389.pdf)
@@ -306,7 +310,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * Courses
   * [Deep Learning Course – Nando de Freitas@Oxford](http://www.computervisiontalks.com/tag/deep-learning-course/)
 
-## Software 
+## Software
 ### Framework
  * Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [[Web]](http://torch.ch/)
  * Caffe: Deep learning framework by the BVLC [[Web]](http://caffe.berkeleyvision.org/)
@@ -315,7 +319,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * MatConvNet: CNNs for MATLAB [[Web]](http://www.vlfeat.org/matconvnet/)
 
 ### Applications
- * Adversarial Training 
+ * Adversarial Training
   * Code and hyperparameters for the paper "Generative Adversarial Networks" [[Web]](https://github.com/goodfeli/adversarial)
  * Understanding and Visualizing
   * Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015. [[Web]](https://github.com/aravindhm/deep-goggle)
@@ -326,7 +330,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Image Super-Resolution for Anime-Style-Art [[Web]](https://github.com/nagadomi/waifu2x)
  * Edge Detection
   * Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015. [[Web]](https://github.com/shenwei1231/DeepContour)
- 
+
 ## Tutorials
   * [CVPR 2014] [Tutorial on Deep Learning in Computer Vision](https://sites.google.com/site/deeplearningcvpr2014/)
   * [CVPR 2015] [Applied Deep Learning for Computer Vision with Torch](http://torch.ch/docs/cvpr15.html)