Deformable Convolutional Networks
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Yi Li | Yichen Wei | Yuwen Xiong | Guodong Zhang | Han Hu | Jifeng Dai | Haozhi Qi | Yichen Wei | Jifeng Dai | Yi Li | Guodong Zhang | Haozhi Qi | Yuwen Xiong | Han Hu
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] Richard Kronland-Martinet,et al. A real-time algorithm for signal analysis with the help of the wavelet transform , 1989 .
[3] Edward H. Adelson,et al. The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Ph. Tchamitchian,et al. Wavelets: Time-Frequency Methods and Phase Space , 1992 .
[5] Deformable Kernels for Early Vision , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[6] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[7] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[8] J. Koenderink,et al. Representation of local geometry in the visual system , 1987, Biological Cybernetics.
[9] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[10] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[11] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[12] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Jean Ponce,et al. A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.
[14] Subhransu Maji,et al. Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.
[15] Ethan Rublee,et al. ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.
[16] Trevor Darrell,et al. Beyond spatial pyramids: Receptive field learning for pooled image features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[17] S. Mallat,et al. Invariant Scattering Convolution Networks , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] Honglak Lee,et al. Learning Invariant Representations with Local Transformations , 2012, ICML.
[19] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[20] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Jitendra Malik,et al. Simultaneous Detection and Segmentation , 2014, ECCV.
[22] Dumitru Erhan,et al. Scalable, High-Quality Object Detection , 2014, ArXiv.
[23] Pedro M. Domingos,et al. Deep Symmetry Networks , 2014, NIPS.
[24] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[25] David W. Jacobs,et al. Locally Scale-Invariant Convolutional Neural Networks , 2014, ArXiv.
[26] Jitendra Malik,et al. Deformable part models are convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Andrea Vedaldi,et al. Understanding Image Representations by Measuring Their Equivariance and Equivalence , 2014, International Journal of Computer Vision.
[28] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[29] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Xiaogang Wang,et al. DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[32] Trevor Darrell,et al. Fully convolutional networks for semantic segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Joachim M. Buhmann,et al. Transformation-Invariant Convolutional Jungles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Jian Sun,et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Nikos Komodakis,et al. Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[36] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[37] Raquel Urtasun,et al. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks , 2016, NIPS.
[38] Joachim M. Buhmann,et al. TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[41] Arnold W. M. Smeulders,et al. Structured Receptive Fields in CNNs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[43] Luc Van Gool,et al. Dynamic Filter Networks , 2016, NIPS.
[44] Koray Kavukcuoglu,et al. Exploiting Cyclic Symmetry in Convolutional Neural Networks , 2016, ICML.
[45] Yi Li,et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.
[46] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Stephan J. Garbin,et al. Harmonic Networks: Deep Translation and Rotation Equivariance , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Simon Lucey,et al. Inverse Compositional Spatial Transformer Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Thomas A. Funkhouser,et al. Dilated Residual Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Sergio Guadarrama,et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[53] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Junmo Kim,et al. Active Convolution: Learning the Shape of Convolution for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.