Paper:机器学习、深度学习常用的国内/国外引用(References)论文参考文献集合(建议收藏,持续更新)

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Paper:机器学习、深度学习常用的国内/国外引用(References)论文参考文献集合(建议收藏,持续更新)

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Paper:机器学习、深度学习常用的国内/国外引用(References)论文参考文献集合(建议收藏,持续更新)

References 1、国外格式

[1] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. [2] T. Cover  P. Hart, "Nearest neighbor pattern classification," Journal IEEE Transactions on Information Theory archive Volume 13 Issue 1, January 1967

2、国内格式

[1] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors.[J]. 1986, 323(6088):399-421. [2] Cover T M, Hart P E. Nearest neighbor pattern classification. IEEE Trans Inf Theory IT-13(1):21-27[J]. IEEE Transactions on Information Theory, 1967, 13(1):21-27. [3] Daral N. Histograms of Oriented Gradients for Human Detection[J]. Proc. of CVPR, 2005, 2005. [3.1] Histograms of Oriented Gradients for Human Detection. Dalai,N,B.Triggs. Computer Vision and Pattern Recognition, 2005.CVPR 2005.IEEE Computer Society Conference on . 2005 [4] Kazemi V, Sullivan J. One Millisecond Face Alignment with an Ensemble of Regression Trees[C] Computer Vision and Pattern Recognition. IEEE, 2014:1867-1874.

[5] David J. Hand and Robert J. Till( 2001). A Simple Generalization of the Area Under the ROC Curve for Multiple Class Classification Problems . Machine Learning , 45(2), 171 – 186 .

一、综合方向

周志华,机器学习,清华大学出版社,2016 李航,统计学习方法,清华大学出版社,2012 Scikit-learn,scikit-learn: machine learning in Python — scikit-learn 1.1.3 documentation Qcon 2017 feature engineering by Gabriel Moreira Thomas M.Cover, JoyA. Thomas. Elementsof InformationTheory. 2006 Christopher M.Bishop. Pattern Recognition and Machine Learning. Springer-Verlag. 2006

二、预测方向 1、ML预测类参考文章

1. sklearn documentation for RandomForestRegressor, http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html 2. Leo Breiman. (2001). “Random Forests.” Machine Learning , 45 (1): 5–32.doi:10.1023/A:10109334043243. J. H. Friedman. “Greedy Function Approximation: A Gradient BoostingMachine,” https://statweb.stanford.edu/~jhf/ftp/trebst.pdf 3. J. H. Friedman. “Greedy Function Approximation: A Gradient Boosting Machine,”https://statweb.stanford.edu/~jhf/ftp/trebst.pdf 4. sklearn documentation for RandomForestRegressor, http://scikit-learn.org/stable/modules/generated/sklearn.ensemble. RandomForestRegressor.html 5. L. Breiman, “Bagging predictors,” http://statistics.berkeley.edu/sites/default/files/techreports/421.pdf 6. Tin Ho. (1998). “The Random Subspace Method for Constructing DecisionForests.”IEEE Transactions on Pattern Analysis and Machine Intelligence ,20 (8): 832–844.doi:10.1109/34.709601 7. J. H. Friedman. “Greedy Function Approximation: A Gradient BoostingMachine,”https://statweb.stanford.edu/~jhf/ftp/trebst.pdf 8. J. H. Friedman. “Stochastic Gradient Boosting,”https://statweb.stanford.edu/~jhf/ftp/stobst.pdf 9. sklearn documentation for GradientBoostingRegressor, http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html 10. J. H. Friedman. “Greedy Function Approximation: A Gradient BoostingMachine,”https://statweb.stanford.edu/~jhf/ftp/trebst.pdf 11. J. H. Friedman. “Stochastic Gradient Boosting,” https://statweb.stanford.edu/~jhf/ftp/stobst.pdf 12. J. H. Friedman. “Stochastic Gradient Boosting,” https://statweb.stanford.edu/~jhf/ftp/stobst.pdf 13. sklearn documentation for RandomForestClassifier, http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html 14. sklearn documentation for GradientBoostingClassifier, http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html

三、CV方向 1、《ImageNet Classification with Deep Convolutional  Neural Networks》

Alex Krizhevsky University of Toronto      Ilya Sutskever University of Toronto       Geoffrey E. Hinton University of Toronto

REFERENCES [1] R.M. Bell and Y. Koren. Lessons from the netflix prize challenge. ACM SIGKDD Explorations Newsletter, 9(2):75–79, 2007. [2] A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge 2010. www.imagenet.org/challenges. 2010. [3] L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001. [4] D. Cire¸san, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. Arxiv preprint arXiv:1202.2745, 2012. [5] D.C. Cire¸san, U. Meier, J. Masci, L.M. Gambardella, and J. Schmidhuber. High-performance neural networks for visual object classification. Arxiv preprint arXiv:1102.0183, 2011. [6] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09, 2009. [7] J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei. ILSVRC-2012, 2012. URL http://www.image-net.org/challenges/LSVRC/2012/. [8] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1):59–70, 2007. [9] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007. URL http://authors.library.caltech.edu/7694. [10] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012. [11] K. Jarrett, K. Kavukcuoglu, M. A. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In International Conference on Computer Vision, pages 2146–2153. IEEE, 2009. [12] A. Krizhevsky. Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto, 2009. [13] A. Krizhevsky. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010. [14] A. Krizhevsky and G.E. Hinton. Using very deep autoencoders for content-based image retrieval. In ESANN, 2011. [15] Y. Le Cun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, et al. Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems, 1990. [16] Y. LeCun, F.J. Huang, and L. Bottou. Learning methods for generic object recognition with invariance to pose and lighting. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II–97. IEEE, 2004. [17] Y. LeCun, K. Kavukcuoglu, and C. Farabet. Convolutional networks and applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pages 253–256. IEEE, 2010. [18] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009. [19] T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka. Metric Learning for Large Scale Image Classifi- cation: Generalizing to New Classes at Near-Zero Cost. In ECCV - European Conference on Computer Vision, Florence, Italy, October 2012. [20] V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc. 27th International Conference on Machine Learning, 2010. [21] N. Pinto, D.D. Cox, and J.J. DiCarlo. Why is real-world visual object recognition hard? PLoS computational biology, 4(1):e27, 2008. [22] N. Pinto, D. Doukhan, J.J. DiCarlo, and D.D. Cox. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS computational biology, 5(11):e1000579, 2009. [23] B.C. Russell, A. Torralba, K.P. Murphy, and W.T. Freeman. Labelme: a database and web-based tool for image annotation. International journal of computer vision, 77(1):157–173, 2008. [24] J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1665–1672. IEEE, 2011. [25] P.Y. Simard, D. Steinkraus, and J.C. Platt. Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the Seventh International Conference on Document Analysis and Recognition, volume 2, pages 958–962, 2003. [26] S.C. Turaga, J.F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H.S. Seung. Convolutional networks can

2、《Faster R-CNN: Towards Real-Time Object  Detection with Region Proposal Networks》

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun

REFERENCES [1] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” in European Conference on Computer Vision (ECCV), 2014. [2] R. Girshick, “Fast R-CNN,” in IEEE International Conference on Computer Vision (ICCV), 2015. [3] K. Simonyan and A. Zisserman, “Very deep convolutionalnetworks for large-scale image recognition,” in International Conference on Learning Representations (ICLR), 2015. [4] J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” International Journal of Computer Vision (IJCV), 2013. [5] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [6] C. L. Zitnick and P. Dollar, “Edge boxes: Locating object ´ proposals from edges,” in European Conference on Computer Vision (ECCV), 2014. [7] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [8] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained partbased models,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010. [9] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” in International Conference on Learning Representations (ICLR), 2014. [10] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Neural Information Processing Systems (NIPS), 2015. [11] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” 2007. [12] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, “Microsoft COCO: Com- ´ mon Objects in Context,” in European Conference on Computer Vision (ECCV), 2014. [13] S. Song and J. Xiao, “Deep sliding shapes for amodal 3d object detection in rgb-d images,” arXiv:1511.02300, 2015. [14] J. Zhu, X. Chen, and A. L. Yuille, “DeePM: A deep part-based model for object detection and semantic part localization,” arXiv:1511.07131, 2015. [15] J. Dai, K. He, and J. Sun, “Instance-aware semantic segmentation via multi-task network cascades,” arXiv:1512.04412, 2015. [16] J. Johnson, A. Karpathy, and L. Fei-Fei, “Densecap: Fully convolutional localization networks for dense captioning,” arXiv:1511.07571, 2015. [17] D. Kislyuk, Y. Liu, D. Liu, E. Tzeng, and Y. Jing, “Human curation and convnets: Powering item-to-item recommendations on pinterest,” arXiv:1511.04003, 2015. [18] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv:1512.03385, 2015. [19] J. Hosang, R. Benenson, and B. Schiele, “How good are detection proposals, really?” in British Machine Vision Conference (BMVC), 2014. [20] J. Hosang, R. Benenson, P. Dollar, and B. Schiele, “What makes ´ for effective detection proposals?” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. [21] N. Chavali, H. Agrawal, A. Mahendru, and D. Batra, “Object-Proposal Evaluation Protocol is ’Gameable’,” arXiv: 1505.05836, 2015. [22] J. Carreira and C. Sminchisescu, “CPMC: Automatic object segmentation using constrained parametric min-cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2012. [23] P. Arbelaez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, ´ “Multiscale combinatorial grouping,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [24] B. Alexe, T. Deselaers, and V. Ferrari, “Measuring the objectness of image windows,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2012. [25] C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in Neural Information Processing Systems (NIPS), 2013. [26] D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, “Scalable object detection using deep neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [27] C. Szegedy, S. Reed, D. Erhan, and D. Anguelov, “Scalable, high-quality object detection,” arXiv:1412.1441 (v1), 2015. [28] P. O. Pinheiro, R. Collobert, and P. Dollar, “Learning to segment object candidates,” in Neural Information Processing Systems (NIPS), 2015. [29] J. Dai, K. He, and J. Sun, “Convolutional feature masking for joint object and stuff segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [30] S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun, “Object detection networks on convolutional feature maps,” arXiv:1504.06066, 2015. [31] J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in Neural Information Processing Systems (NIPS), 2015. [32] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional neural networks,” in European Conference on Computer Vision (ECCV), 2014. [33] V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in International Conference on Machine Learning (ICML), 2010. [34] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [35] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation, 1989. [36] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” in International Journal of Computer Vision (IJCV), 2015. [37] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classi- fication with deep convolutional neural networks,” in Neural Information Processing Systems (NIPS), 2012. [38] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv:1408.5093, 2014. [39] K. Lenc and A. Vedaldi, “R-CNN minus R,” in British Machine Vision Conference (BMVC), 2015.

3、《Mask R-CNN》

Kaiming He Georgia Gkioxari Piotr Dollar Ross Girshick ´ Facebook AI Research (FAIR)

References [1] M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele. 2D human pose estimation: New benchmark and state of the art analysis. In CVPR, 2014. 8 [2] P. Arbelaez, J. Pont-Tuset, J. T. Barron, F. Marques, and ´ J. Malik. Multiscale combinatorial grouping. In CVPR, 2014. 2 [3] A. Arnab and P. H. Torr. Pixelwise instance segmentation with a dynamically instantiated network. In CVPR, 2017. 3, 9 [4] M. Bai and R. Urtasun. Deep watershed transform for instance segmentation. In CVPR, 2017. 3, 9 [5] S. Bell, C. L. Zitnick, K. Bala, and R. Girshick. Insideoutside net: Detecting objects in context with skip pooling and recurrent neural networks. In CVPR, 2016. 5 [6] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multiperson 2d pose estimation using part affinity fields. In CVPR, 2017. 7, 8 [7] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele. The Cityscapes dataset for semantic urban scene understanding. In CVPR, 2016. 9 [8] J. Dai, K. He, Y. Li, S. Ren, and J. Sun. Instance-sensitive fully convolutional networks. In ECCV, 2016. 2 [9] J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In CVPR, 2015. 2 [10] J. Dai, K. He, and J. Sun. Instance-aware semantic segmentation via multi-task network cascades. In CVPR, 2016. 2, 3, 4, 5, 6 [11] J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object detection via region-based fully convolutional networks. In NIPS, 2016. 2 [12] R. Girshick. Fast R-CNN. In ICCV, 2015. 1, 2, 3, 4, 6 [13] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 2, 3 [14] R. Girshick, F. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. In CVPR, 2015. 4 [15] B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik. Simul- ´ taneous detection and segmentation. In ECCV. 2014. 2 [16] B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik. Hyper- ´ columns for object segmentation and fine-grained localization. In CVPR, 2015. 2 [17] Z. Hayder, X. He, and M. Salzmann. Shape-aware instance segmentation. In CVPR, 2017. 9 [18] K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV. 2014. 1, 2 [19] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 2, 4, 7, 10 [20] J. Hosang, R. Benenson, P. Dollar, and B. Schiele. What ´ makes for effective detection proposals? PAMI, 2015. 2 [21] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al. Speed/accuracy trade-offs for modern convolutional object detectors. In CVPR, 2017. 2, 3, 4, 6, 7 [22] M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu. Spatial transformer networks. In NIPS, 2015. 4 [23] A. Kirillov, E. Levinkov, B. Andres, B. Savchynskyy, and C. Rother. Instancecut: from edges to instances with multicut. In CVPR, 2017. 3, 9 [24] A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. 2 [25] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989. 2 [26] Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei. Fully convolutional instance-aware semantic segmentation. In CVPR, 2017. 2, 3, 5, 6 [27] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and ´ S. Belongie. Feature pyramid networks for object detection. In CVPR, 2017. 2, 4, 5, 7 [28] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft COCO: Com- ´ mon objects in context. In ECCV, 2014. 2, 5 [29] S. Liu, J. Jia, S. Fidler, and R. Urtasun. SGN: Sequential grouping networks for instance segmentation. In ICCV, 2017. 3, 9 [30] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1, 3, 6 [31] V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010. 4 [32] G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy. Towards accurate multiperson pose estimation in the wild. In CVPR, 2017. 8 [33] P. O. Pinheiro, R. Collobert, and P. Dollar. Learning to segment object candidates. In NIPS, 2015. 2, 3 [34] P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollar. Learn- ´ ing to refine object segments. In ECCV, 2016. 2, 3 [35] I. Radosavovic, P. Dollar, R. Girshick, G. Gkioxari, and ´ K. He. Data distillation: Towards omni-supervised learning. arXiv:1712.04440, 2017. 10 [36] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS, 2015. 1, 2, 3, 4, 7 [37] S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In TPAMI, 2017. 10 [38] A. Shrivastava, A. Gupta, and R. Girshick. Training regionbased object detectors with online hard example mining. In CVPR, 2016. 2, 5 [39] A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta. Beyond skip connections: Top-down modulation for object detection. arXiv:1612.06851, 2016. 4, 7 [40] C. Sun, A. Shrivastava, S. Singh, and A. Gupta. Revisiting unreasonable effectiveness of data in deep learning era. In ICCV, 2017. 10 [41] C. Szegedy, S. Ioffe, and V. Vanhoucke. Inception-v4, inception-resnet and the impact of residual connections on learning. In ICLR Workshop, 2016. 7 [42] J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. IJCV, 2013. 2 [43] X. Wang, R. Girshick, A. Gupta, and K. He. Non-local neural networks. arXiv:1711.07971, 2017. 10 [44] S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh. Convolutional pose machines. In CVPR, 2016. 8 [45] S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He. Aggregated ´ residual transformations for deep neural networks. In CVPR, 2017. 4, 10

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