Fix outdated arXiv links

README.md

@@ -45,11 +45,11 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![classification](https://cloud.githubusercontent.com/assets/5226447/8451949/327b9566-2022-11e5-8b34-53b4a64c13ad.PNG)
 (from Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.)
 
-  * Microsoft (PReLu/Weight Initialization) [[Paper]](http://arxiv.org/pdf/1502.01852v1)
+  * Microsoft (PReLu/Weight Initialization) [[Paper]](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.
-  * Batch Normalization [[Paper]](http://arxiv.org/pdf/1502.03167v3)
+  * 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/1409.4842v1)
+  * 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. 
   * 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.
@@ -60,9 +60,9 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![object_detection](https://cloud.githubusercontent.com/assets/5226447/8452063/f76ba500-2022-11e5-8db1-2cd5d490e3b3.PNG)
 (from Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.)
 
-  * OverFeat, NYU [[Paper]](http://arxiv.org/pdf/1311.2901v3)
+  * OverFeat, NYU [[Paper]](http://arxiv.org/pdf/1311.2901)
    * Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014.
-  * R-CNN, UC Berkeley [[Paper-CVPR14]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) [[Paper-arXiv14]](http://arxiv.org/pdf/1311.2524v5)
+  * R-CNN, UC Berkeley [[Paper-CVPR14]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) [[Paper-arXiv14]](http://arxiv.org/pdf/1311.2524)
    * Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
   * SPP, Microsoft Research [[Paper]](http://arxiv.org/pdf/1406.4729)
    * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
@@ -79,15 +79,15 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
  * N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. [[Paper]](http://winsty.net/papers/dlt.pdf)
 
 ### Low-Level Vision
- * Optical Flow (FlowNet) [[Paper]](http://arxiv.org/pdf/1504.06852v2)
+ * Optical Flow (FlowNet) [[Paper]](http://arxiv.org/pdf/1504.06852)
   * 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.
- * 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/1501.00092v1.pdf)[[Paper ICONIP-2014]](http://www.brml.org/uploads/tx_sibibtex/281.pdf)
+ * 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)[[Paper ICONIP-2014]](http://www.brml.org/uploads/tx_sibibtex/281.pdf)
     * 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.
     * Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014. 
- * Compression Artifacts Reduction [[Paper-arXiv15]](http://arxiv.org/pdf/1504.06993v1)
+ * 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/1503.00593v3)
+ * 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. 
  * 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.
@@ -100,9 +100,9 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![edge_detection](https://cloud.githubusercontent.com/assets/5226447/8452371/93ca6f7e-2025-11e5-90f2-d428fd5ff7ac.PNG)
 (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/1504.06375v1)
+ * Holistically-Nested Edge Detection [[Paper]](http://arxiv.org/pdf/1504.06375)
   * Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375. 
- * DeepEdge [[Paper]](http://arxiv.org/pdf/1412.1123v3)
+ * 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)
   * 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.
@@ -111,11 +111,11 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![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.)
 
-  * BoxSup [[Paper]](http://arxiv.org/pdf/1503.01640v2)
+  * 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.
-  * Conditional Random Fields as Recurrent Neural Networks [[Paper]](http://arxiv.org/pdf/1502.03240v2)
+  * 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.
-  * 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/1411.4038v2)
+  * 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.
   * 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.
@@ -134,7 +134,7 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
    * Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.
   * Learning a Sequential Search for Landmarks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf)
    * Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.
-  * Multiple Object Recognition with Visual Attention [[Paper]](http://arxiv.org/pdf/1412.7755v2.pdf)
+  * Multiple Object Recognition with Visual Attention [[Paper]](http://arxiv.org/pdf/1412.7755.pdf)
    * 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.
@@ -161,44 +161,44 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 ![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.)
 
-   * Baidu / UCLA [[Paper]](http://arxiv.org/pdf/1410.1090v1)
+   * UCLA / Baidu [[Paper]](http://arxiv.org/pdf/1410.1090)
       * Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, Explain Images with Multimodal Recurrent Neural Networks, arXiv:1410.1090.
-   * Toronto [[Paper]](http://arxiv.org/pdf/1411.2539v1)
+   * Toronto [[Paper]](http://arxiv.org/pdf/1411.2539)
       * Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, arXiv:1411.2539.
-   * Berkeley [[Paper]](http://arxiv.org/pdf/1411.4389v3)
+   * Berkeley [[Paper]](http://arxiv.org/pdf/1411.4389)
       * 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, arXiv:1411.4389.
-   * Google [[Paper]](http://arxiv.org/pdf/1411.4555v2)
+   * Google [[Paper]](http://arxiv.org/pdf/1411.4555)
       * Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, Show and Tell: A Neural Image Caption Generator, arXiv:1411.4555.
    * 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/1412.4729v3)
+   * 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. 
-   * Microsoft / CMU [[Paper]](http://arxiv.org/pdf/1411.5654v1)
+   * CMU / Microsoft [[Paper]](http://arxiv.org/pdf/1411.5654)
       * Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generation, arXiv:1411.5654.
-   * Microsoft [[Paper]](http://arxiv.org/pdf/1411.4952v3)
+   * 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. 
 
 ### Video Captioning
-* Berkeley [[Web]](http://jeffdonahue.com/lrcn/) [[Paper]](http://arxiv.org/pdf/1411.4389v3.pdf)
+* Berkeley [[Web]](http://jeffdonahue.com/lrcn/) [[Paper]](http://arxiv.org/pdf/1411.4389.pdf)
   * 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.
-* UT / UML / Berkeley [[Paper]](http://arxiv.org/pdf/1412.4729v3.pdf)
+* UT / UML / Berkeley [[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, arXiv:1412.4729.
-* Microsoft [[Paper]](http://arxiv.org/pdf/1505.01861v1.pdf)
+* Microsoft [[Paper]](http://arxiv.org/pdf/1505.01861)
   * Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.
-* UT / Berkeley / UML [[Paper]](http://arxiv.org/pdf/1505.00487v2.pdf)
+* UT / Berkeley / UML [[Paper]](http://arxiv.org/pdf/1505.00487)
   * Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence--Video to Text, arXiv:1505.00487.
 
 ### Question Answering
 ![question_answering](https://cloud.githubusercontent.com/assets/5226447/8452068/ffe7b1f6-2022-11e5-87ab-4f6d4696c220.PNG)
 (from Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop)
 
-* MSR / Virginia Tech. [[Web]](http://www.visualqa.org/) [[Paper]](http://arxiv.org/pdf/1505.00468v1.pdf)
+* Virginia Tech / MSR [[Web]](http://www.visualqa.org/) [[Paper]](http://arxiv.org/pdf/1505.00468)
   * Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop.
-* MPI / Berkeley [[Web]](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/) [[Paper]](http://arxiv.org/pdf/1505.01121v2.pdf)
+* MPI / Berkeley [[Web]](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/) [[Paper]](http://arxiv.org/pdf/1505.01121)
   * Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, arXiv:1505.01121.
-* Toronto [[Paper]](http://arxiv.org/pdf/1505.02074v1.pdf) [[Dataset]](http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)
+* Toronto [[Paper]](http://arxiv.org/pdf/1505.02074) [[Dataset]](http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)
   * Mengye Ren, Ryan Kiros, Richard Zemel, Image Question Answering: A Visual Semantic Embedding Model and a New Dataset, arXiv:1505.02074 / ICML 2015 deep learning workshop.
-* Baidu / UCLA [[Paper]](http://arxiv.org/pdf/1505.05612v1.pdf) [[Dataset]]()
+* Baidu / UCLA [[Paper]](http://arxiv.org/pdf/1505.05612) [[Dataset]]()
   * Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu, Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, arXiv:1505.05612.
 
 ### Other Topics