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DAVIDSTUTZ

Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
  • Anh Mai Nguyen, Jason Yosinski, Jeff Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR, 2015.   Reading Notes
  • Min Lin, Qiang Chen, Shuicheng Yan. Network In Network. CoRR; 2013.   Reading Notes
  • Vishakh Hegde, Reza Zadeh. FusionNet: 3D Object Classification Using Multiple Data Representations. CoRR, 2016.   Reading Notes
  • Zeeshan Hayder, Xuming He, Mathieu Salzmann. Shape-aware Instance Segmentation. CoRR, 2016.   Reading Notes
  • Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: 3DV, 2016.   Reading Notes
  • Bo Li. 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. CoRR, 2016.   Reading Notes
  • Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CoRR, 2016.   Reading Notes
  • Alberto Garcia-Garcia, Francisco Gomez-Donoso, José García Rodríguez, Sergio Orts-Escolano, Miguel Cazorla, Jorge Azorín López. PointNet: A 3D Convolutional Neural Network for real-time object class recognition. IJCNN, 2016.   Reading Notes
  • Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia. Multi-View 3D Object Detection Network for Autonomous Driving. CoRR, 2016.   Reading Notes
  • Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, Abhinav Gupta. Learning a Predictable and Generative Vector Representation for Objects. CoRR, 2016.   Reading Notes
  • M. Savva, F. Yu, Hao Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, Hang Su, S. Bai, X. Bai, N. Fish, J. Han, E. Kalogerakis, E. G. Learned-Miller, Y. Li, M. Liao, S. Maji, A. Tatsuma, Y. Wang, N. Zhang and Z. Zhou. SHREC’16 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55. Eurographics Workshop on 3D Object Retrieval (2016).   Reading Notes
  • Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner. Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis. CoRR, 2016.   Reading Notes
  • Alexandr Notchenko, Ermek Kapushev, Evgeny Burnaev. Sparse 3D Convolutional Neural Networks for Large-Scale Shape Retrieval. CoRR, 2016.   Reading Notes
  • Abhishek Sharma, Oliver Grau, Mario Fritz. VConv-DAE: Deep Volumetric Shape Learning Without Object Labels. ECCV Workshops, 2016.   Reading Notes
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  • W. Kuo, B. Hariharan, J. Malik. DeepBox: Learning Objectness with Convolutional Networks. ICCV, 2015.   Reading Notes
  • L. J. Ba, R. Kiros, G. E. Hinton. Layer Normalization. CoRR, 2016.   Reading Notes
  • I. Misra, C. L. Zitnick, M. Hebert. Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification. ECCV, 2016.   Reading Notes
  • A. M. Saxe, J. L. McClelland, S. Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. CoRR, 2013.   Reading Notes
  • D. Mishkin, J. Matas. All you need is a good init. CoRR, 2015.   Reading Notes
  • M. Ersin Yumer, N. J. Mitra. Learning Semantic Deformation Flows with 3D Convolutional Networks. ECCV, 2016.   Reading Notes
  • S. Ioffe, C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML, 2015.   Reading Notes
  • M. Noroozi, P. Favaro. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. ECCV,2016.   Reading Notes
  • V. Dumoulin, F. Visin. A guide to convolution arithmetic for deep learning. CoRR, 2016.   Reading Notes
  • C. Doersch, A. Gupta, A. A. Efros. Unsupervised Visual Representation Learning by Context Prediction. ICCV, 2015.   Reading Notes
  • I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. C. Courville, Y. Bengio. Maxout Networks. ICML, 2013.   Reading Notes
  • R. Pascanu, T. Mikolov, Y. Bengio. On the difficulty of training recurrent neural networks. ICML, 2013.   Reading Notes
  • A. G. Howard. Some Improvements on Deep Convolutional Neural Network Based Image Classification. CoRR, 2013.   Reading Notes
  • Y. Hoshen, S. Peleg. Visual Learning of Arithmetic Operations. CoRR, 2015.   Reading Notes
  • B. Xu, N. Wang, T. Chen, M. Li. Empirical Evaluation of Rectified Activations in Convolutional Network. CoRR, 2015.   Reading Notes
  • C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the Inception Architecture for Computer Vision. CVPR, 2016.   Reading Notes
  • C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going deeper with convolutions. CVPR, 2015.   Reading Notes
  • P. Krähenbühl, V. Koltun. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. NIPS, 2011.   Reading Notes
  • A. G. Schwing, R. Urtasun. Fully Connected Deep Structured Networks. CoRR, 2015.   Reading Notes
  • L.-C. Chen, A. G. Schwing, A. L. Yuille, R. Urtasun. Learning Deep Structured Models. ICML, 2015.   Reading Notes
  • R. Socher, B. Huval, B. P. Bath, C. D. Manning, A. Y. Ng. Convolutional-Recursive Deep Learning for 3D Object Classification. NIPS, 2012.   Reading Notes
  • M. Engelcke, D. Rao, D. Zeng Wang, C. H. Tong, I. Posner. Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, 2016.   Reading Notes
  • J. Huang, S. You. Point cloud labeling using 3d convolutional neural network. ICPR, 2016.   Reading Notes
  • A. Brock, T. Lim, J. M. Ritchie, N. Weston. Generative and Discriminative Voxel Modeling with Convolutional Neural Networks. CoRR, 2016.   Reading Notes
  • S. Song, J. Xiao. Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images. CVPR, 2016.   Reading Notes
  • Nima Sedaghat Alvar, Mohammadreza Zolfaghari, Thomas Brox. Orientation-boosted Voxel Nets for 3D Object Recognition. CoRR, abs/1604.03351, 2016.   Reading Notes
  • Diederik P. Kingma, Max Welling. Auto-Encoding Variational Bayes- CoRR, abs/1312.6114.   Reading Notes
  • Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Ole Winther. Autoencoding beyond pixels using a learned similarity metric. ICML, 2016.   Reading Notes
  • Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. CoRR, 2016.   Reading Notes
  • Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik G. Learned-Miller. Multi-view Convolutional Neural Networks for 3D Shape Recognition. CoRR, 2015.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. 3D Convolutional Neural Networks for Landing Zone Detection from LiDAR. ICRA, 2015.   Reading Notes
  • Ben Graham. Sparse 3D convolutional neural networks. BMVC, 2015.   Reading Notes
  • Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao. 3D ShapeNets: A Deep Representation for Volumetric Shape Modeling. CVPR, 2015.   Reading Notes
  • Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas. FPNN: Field Probing Neural Networks for 3D Data. CoRR, abs/1605.06240.   Reading Notes
  • Charles Ruizhongtai Qi. Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, Leonidas Guibas. Volumetric and Multi-View CNNs for Object Classification on 3D Data. CVPR, 2016.   Reading Notes
  • Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V. C. T. Mok, L. Shi, P.-A. Heng. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans. Med. Imaging, 2016.   Reading Notes
  • H. Chen, Q. Dou, L. Yu, P.-A. Heng. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. CoRR, 2016.   Reading Notes
  • B. Li, T. Zhang, T. Xia. Vehicle Detection from 3D Lidar Using Fully Convolutional Network. Robotics: Science and Systems, 2016.   Reading Notes
  • Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu,David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio. Generative Adversarial Networks. CoRR, abs/1406.2661.   Reading Notes
  • Daniel Maturana, Sebastian Scherer. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. IROS, 2015.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 20, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 19, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 18, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 17, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 16, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 15, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 11, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 10, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 9, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 8, MIT Press, 2016.   Reading Notes
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. Chapter 7, MIT Press, 2016.   Reading Notes
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  • P. Márquez-Neila, P. Kohli, C. Rother, L. Baumela. Non-parametric Higher-Order Random Fields for Image Segmentation. ECCV, 2014.   Reading Notes
  • P. Arbeláez J. Pont-Tuset, J. T. Barron, F. Marqués, J. Malik. Multiscale Combinatorial Grouping.Conference on Computer Vision and Pattern Recognition, 2014.   Reading Notes
  • P. Arbelaez. Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Conference on Computer Vision and Pattern Recognition, 2006.   Reading Notes
  • A. Koenig, B. Moo. C++ Made Easier: The Rule of Three. 2001.   Reading Notes
  • M. Fowler. Inversion of Control Containers and the Dependency Injection Pattern. 2004.   Reading Notes
  • J. Yip. It's Not Just Standing Up: Patterns for Daily Standup Meetings. 2006.   Reading Notes
  • M. Johnson, K. Hofmann, T. Hutton, D. Bignell. The Malmo Platform for Artificial Intelligence Experimentation. International Joint Conference on Artificial Intelligence, 2015.   Reading Notes
  • J. Mairal, F. Bach, J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision, colume 8, 2014.   Reading Notes
  • M. Menze, A. Geiger. Object scene flow for autonomous vehicles. Conference on Computer Vision and Pattern Recognition, 2015.   Reading Notes
  • L. Sevilla-Lara, D. Sun, V. Jampani, M. J. Black. Optical Flow with Semantic Segmentation and Localized Layers. Computing Research Repository, abs/1603.03911, 2016.   Reading Notes
  • M. Kiefel, V. Jampani, P. V. Gehler. Permutohedral Lattice CNNs. Computing Research Repository, abs/1412.6618, 2014.   Reading Notes
  • David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, November 2004.   Reading Notes
  • O. Chum, J. Philbin, J. Sivic, M. Isard, A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In Computer Vision, International Conference on, pages 1–8, Rio de Janeiro, Brazil, October 2007.   Reading Notes
  • C. Harris, M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, pages 1–6, Manchester, United Kingdom, September 1988.   Reading Notes
  • G. Kutyniok. Theory and Applications of Compressed Sensing. Computing Research Repository, abs/1203.3815, 2012.   Reading Notes
  • S. Vedula, S. Baker, P. Rander, R. T. Collins, T. Kanade. Three-Dimensional Scene Flow. Transactions on Pattern Analysis and Machine Intelligence, 2005.   Reading Notes
  • A. Geiger, P. Lenz, R. Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark. Conference on Computer Vision and Pattern Recognition, 2012.   Reading Notes
  • J. Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts for Intelligent Vision Systems, 2005.   Reading Notes
  • P. L. Combettes and J.-C. Pesquet.Proximal Splitting Methods in Signal Processing. Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer, 2011.   Reading Notes
  • Y. Chen, T. Pock, R. Ranftl, H. Bischof. Revisiting Loss-Specific Training of Filter-Based MRFs for Image Restoration. German Conference on Pattern Recognition, 2015.   Reading Notes
  • P. Ochs, Y. Chen,T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Non-Convex Optimization. Computing Research Repository, abs/1404.4805, 2014.   Reading Notes
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  • J. Demsar. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, volume 7, 2006.   Reading Notes
  • A. Babenko, A. Slesarev, A. Chigorin, V. S. Lempitsky. Neural codes for image retrieval. In Computer Vision, European Conference on, volume 8689 of Lecture Notes in Computer Science, pages 584–599, Zurich, Switzerland, September 2014. Springer.   Reading Notes
  • H. Jégou, M. Douze, C. Schmid. Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1):117–128, 2011.   Reading Notes
  • A. Gordo, J. A. Rodríguez-Serrano, F. Perronnin, E. Valveny. Leveraging category-level labels for instance-level image retrieval. In Computer Vision and Pattern Recognition, Conference on, pages 3045–3052, Providence, Rhode Island, June 2012.   Reading Notes
  • M. J. Zaki, W. Meira Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.   Reading Notes
  • H. Jégou, A. Zisserman. Triangulation embedding and democratic aggregation for image search. In Computer Vision and Pattern Recognition, Conference on, pages 3310–3317, Columbus, June 2014.   Reading Notes
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  • S. B. Lippman, J. Lajoie, B. E. Moo. C++ Primer (4th Edition). Addison-Wesley Professional, 2005.   Reading Notes
  • H. Jégou, M. Douze, C. C. Schmid, P. Pérez. Aggregating local descriptors into a compact image representation. In Computer Vision and Pattern Recognition, Conference on, pages 3304–3311, San Fransisco, California, June 2010.   Reading Notes
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  • J. Gama. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 2010.   Reading Notes
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  • A. Babenko, A. Slesarev, A. Chigorin, and V. S. Lempitsky. Neural codes for image retrieval. In Conference on Computer Vision, pages 584–599, 2014.   Reading Notes
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  • S. Beigpour, M. Serra, J. van de Weijer, R. Benavente, M. Vanrell, O. Penacchio, D. Samaras. Intrinsic Image Evaluation on Synthetic Complex Scenes. International Conference on Image Processing, 2013.   Reading Notes
  • M. Y. Lui, O. Tuzel, S. Ramalingam, R. Chellappa. Entropy rate superpixel segmentation. Conference on Computer Vision and Pattern Recognition, 2011.   Reading Notes
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  • A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi. TurboPixels: Fast superpixels using geometric flows. Transactions on Pattern Analysis and Machine Intelligence, 31(12):2290–2297, 2009.   Reading Notes
  • A. Vedaldi, S. Soatto. Quick Shift and Kernel Methods for Mode Seeking. European Conference on Computer Vision, 2008.   Reading Notes
  • K. Leyton-Brown, Y. Shoham. Essentials of Game Theory: A Concise, Multidisciplinary Introduction. Morgan & Claypool Publishers, 2008.   Reading Notes
  • D. Easley, J.Kleinberg. Networks, Crowds and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010.   Reading Notes
  • P. F. Felzenswalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, volume 59, number 2, 2004.   Reading Notes
  • N. C. Oza. Online Bagging and Boosting. International Conference on Systems, Man and Cybernetics, 2005.   Reading Notes
  • A. A. Amini, T. E. Weymouth, R. C. Jain. Using Dynamic Programming for Solving Variational Problems in Vision. Transactions on Pattern Analysis and Machine Intelligence, 2002.   Reading Notes
  • J. Shi, J. Malik. Normalized Cuts and Image Segmentation. Transactions on Pattern Analysis and Machine Intelligence, 2008.   Reading Notes
  • S. Gupta, P. Arbelaez, J. Malik. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images. Conference on Computer Vision and Pattern Recognition, 2013.   Reading Notes
  • S. R. Bulò, P. Kontschieder. Neural Decision Forests for Semantic Image Labelling. Conference on Computer Vision and Pattern Recognition, 2014.   Reading Notes
  • R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk. SLIC Superpixels Compared to State-Of-The-Art Superpixel Methods. Transactions on Pattern Analysis and Machine Intelligence, pages 2274 – 2282, 2012.   Reading Notes
  • M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. van Gool. SEEDS – Superpixels Extracted via Energy-Driven Sampling. European Conference on Computer Vision, pages 13- 26, 2012.   Reading Notes