Fetching the paper…
Reading the bibliography…
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images.
P. V. Hough, “Method and means for recognizing complex patterns,” U.S. Patent, 3069654 , 1962
1962
Earlier work this paper cites.
M. Fischler and R. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , 1981
1981
Earlier work this paper cites.
D. H. Ballard, “Generalizing the hough transform to detect arbitrary shapes,” Pattern Recognition , vol. 13, 1981
1981
Earlier work this paper cites.
G. Donato and S. Belongie, “Approximate thin plate spline mappings,” in Proc. European Conference on Computer Vision (ECCV) , 2002
2002
Earlier work this paper cites.
B. Leibe and B. Schiele, “Interleaved object categorization and segmentation,” in Proc. British Machine Vision Conference (BMVC) , 2003
2003
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision (IJCV) , 2004
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2005
2005
Earlier work this paper cites.
H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in Proc. European Conference on Computer Vision (ECCV) , 2006
2006
Earlier work this paper cites.
J. Gall and V. Lempitsky, “Class-specific hough forests for object detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009
2009
Earlier work this paper cites.
M. Cho, J. Lee, and K. M. Lee, “Reweighted random walks for graph matching,” in Proc. European Conference on Computer Vision (ECCV) , 2010
2010
Earlier work this paper cites.
M. Sun, G. Bradski, B.-X. Xu, and S. Savarese, “Depth-encoded hough voting for joint object detection and shape recovery,” in Proc. European Conference on Computer Vision (ECCV) , 2010
2010
Earlier work this paper cites.
J. Knopp, M. Prasad, and L. Van Gool, “Orientation invariant 3d object classification using hough transform based methods,” in Proceedings of the ACM Workshop on 3D Object Retrieval , 2010
2010
Earlier work this paper cites.
D. Forsyth and J. Ponce, Computer Vision: A Modern Approach. (Second edition) . Prentice Hall, Nov. 2011. [Online]. Available: https://hal.inria.fr/hal-01063327
2011
Earlier work this paper cites.
J. Knopp, M. Prasad, and L. V. Gool, “Scene cut: Class-specific object detection and segmentation in 3d scenes,” International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission , 2011
2011
Earlier work this paper cites.
C. Liu, J. Yuen, and A. Torralba, “Sift flow: Dense correspondence across scenes and its applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 2012
2012
Earlier work this paper cites.
M. Cho and K. M. Lee, “Progressive graph matching: Making a move of graphs via probabilistic voting,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Earlier work this paper cites.
M. Cho, K. Alahari, and J. Ponce, “Learning graphs to match,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2013
2013
Earlier work this paper cites.
H.-Y. Chen, Y.-Y. Lin, and B.-Y. Chen, “Robust feature matching with alternate hough and inverted hough transforms,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2013
2013
Earlier work this paper cites.
J. Kim, C. Liu, F. Sha, and K. Grauman, “Deformable spatial pyramid matching for fast dense correspondences,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
M. Cho, S. Kwak, C. Schmid, and J. Ponce, “Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Earlier work this paper cites.
S. Kwak, M. Cho, I. Laptev, J. Ponce, and C. Schmid, “Unsupervised object discovery and tracking in video collections,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Earlier work this paper cites.
H. Bristow, J. Valmadre, and S. Lucey, “Dense semantic correspondence where every pixel is a classifier,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. v. d. Smagt, D. Cremers, and T. Brox, “Flownet: Learning optical flow with convolutional networks,” in 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 2758–2766
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 , May 2015
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR) , 2015
2015
Cited alongside, same era.
P. H. Seo, J. Lee, D. Jung, B. Han, and M. Cho, “Attentive semantic alignment with offset-aware correlation kernels,” in Proc. European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Later among the works it cites.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 6848–6856. [Online]. Available: https://ieeexplore.ieee.org/document/8578814
2018
Later among the works it cites.
S. Kim, S. Lin, S. Jeon, D. Min, and K. Sohn, “Recurrent transformer networks for semantic correspondence,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018
2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Cited alongside, same era.
W. Kehl, F. Milletari, F. Tombari, S. Ilic, and N. Navab, “Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation,” in Proc. European Conference on Computer Vision (ECCV) , 2016
2016
Cited alongside, same era.
B. Ham, M. Cho, C. Schmid, and J. Ponce, “Proposal flow,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Cited alongside, same era.
W. Sultani and M. Shah, “What if we do not have multiple videos of the same action? — video action localization using web images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Cited alongside, same era.
T. Taniai, S. N. Sinha, and Y. Sato, “Joint recovery of dense correspondence and cosegmentation in two images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Cited alongside, same era.
C. Choy, J. Gwak, S. Savarese, and M. Chandraker, “Universal correspondence network,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016
2016
Cited alongside, same era.
G. Huang*, Z. Liu*, L. van der Maaten, and K. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Cited alongside, same era.
K. Han, R. S. Rezende, B. Ham, K.-Y. K. Wong, M. Cho, C. Schmid, and J. Ponce, “Scnet: Learning semantic correspondence,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2017
2017
Cited alongside, same era.
Later among the works it cites.
S. Huang, Q. Wang, S. Zhang, S. Yan, and X. He, “Dynamic context correspondence network for semantic alignment,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
J. Lee, D. Kim, J. Ponce, and B. Ham, “Sfnet: Learning object-aware semantic correspondence,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
J. Min, J. Lee, J. Ponce, and M. Cho, “Hyperpixel flow: Semantic correspondence with multi-layer neural features,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
X. Wang, A. Jabri, and A. A. Efros, “Learning correspondence from the cycle-consistency of time,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
G. Yang and D. Ramanan, “Volumetric correspondence networks for optical flow,” in Advances in Neural Information Processing Systems (NeurIPS) . Curran Associates, Inc., 2019, pp. 794–805. [Online]. Available: http://papers.nips.cc/paper/8367-volumetric-correspondence-networks-for-optical-flow.pdf
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Choy, J. Park, and V. Koltun, “Fully convolutional geometric features,” in Proc. IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
Y. Liu, L. Zhu, M. Yamada, and Y. Yang, “Semantic correspondence as an optimal transport problem,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Later among the works it cites.
——, “Learning to compose hypercolumns for visual correspondence,” in Proc. European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
M. Fey, J. E. Lenssen, C. Morris, J. Masci, and N. M. Kriege, “Deep graph matching consensus,” in International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
M. Rolínek, P. Swoboda, D. Zietlow, A. Paulus, V. Musil, and G. Martius, “Deep graph matching via blackbox differentiation of combinatorial solvers,” in Proc. European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
S. Li, K. Han, T. W. Costain, H. Howard-Jenkins, and V. Prisacariu, “Correspondence networks with adaptive neighbourhood consensus,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Later among the works it cites.
P. Truong, M. Danelljan, and R. Timofte, “GLU-Net: Global-local universal network for dense flow and correspondences,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Later among the works it cites.
S. Jeon, D. Min, S. Kim, J. Choe, and K. Sohn, “Guided semantic flow,” in Proc. European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
I. Rocco, R. Arandjelović, and J. Sivic, “Efficient neighbourhood consensus networks via submanifold sparse convolutions,” in Proc. European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
2021
Closest in time.
J. Min and M. Cho, “Convolutional hough matching networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 2940–2950
2021
Closest in time.
2021
Closest in time.
X. Li, D.-P. Fan, F. Yang, A. Luo, H. Cheng, and Z. Liu, “Probabilistic model distillation for semantic correspondence,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 7505–7514
2021
Closest in time.
J. Y. Lee, J. DeGol, V. Fragoso, and S. N. Sinha, “Patchmatch-based neighborhood consensus for semantic correspondence,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 13 153–13 163
2021
Closest in time.