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Establishing visual correspondence across images is a challenging and essential task.
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). vol. 2, pp. 1735–1742. IEEE (2006)
2006
Earlier work this paper cites.
Gould, S., Arfvidsson, J., Kaehler, A., Sapp, B., Messner, M., Bradski, G.R., Baumstarck, P., Chung, S., Ng, A.Y., et al.: Peripheral-foveal vision for real-time object recognition and tracking in video. In: Ijcai. vol. 7, pp. 2115–2121 (2007)
2007
Earlier work this paper cites.
Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: Sift flow: Dense correspondence across different scenes. In: European conference on computer vision. pp. 28–42. Springer (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88
2010
Earlier work this paper cites.
Grabner, H., Matas, J., Van Gool, L., Cattin, P.: Tracking the invisible: Learning where the object might be. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1285–1292. IEEE (2010)
2010
Earlier work this paper cites.
Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR 2011. pp. 1385–1392. IEEE (2011)
2011
Earlier work this paper cites.
Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: Proceedings of the IEEE international conference on computer vision. pp. 3192–3199 (2013)
2013
Earlier work this paper cites.
Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. Advances in neural information processing systems (2013)
2013
Earlier work this paper cites.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE transactions on pattern analysis and machine intelligence 37
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Long, J.L., Zhang, N., Darrell, T.: Do convnets learn correspondence? Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2758–2766 (2015)
2015
Earlier work this paper cites.
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 447–456 (2015)
2015
Earlier work this paper cites.
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: European conference on computer vision. pp. 850–865. Springer (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: European conference on computer vision. pp. 749–765. Springer (2016)
2016
Earlier work this paper cites.
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 724–732 (2016)
2016
Earlier work this paper cites.
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 221–230 (2017)
2017
Earlier work this paper cites.
Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6299–6308 (2017)
2017
Earlier work this paper cites.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2462–2470 (2017)
2017
Earlier work this paper cites.
Iqbal, U., Garbade, M., Gall, J.: Pose for action-action for pose. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). pp. 438–445. IEEE (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Ranjan, A., Black, M.J.: Optical flow estimation using a spatial pyramid network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4161–4170 (2017)
2017
Earlier work this paper cites.
Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6148–6157 (2017)
2017
Earlier work this paper cites.
Song, J., Wang, L., Van Gool, L., Hilliges, O.: Thin-slicing network: A deep structured model for pose estimation in videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4220–4229 (2017)
2017
Earlier work this paper cites.
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2805–2813 (2017)
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Chen, Y.C., Huang, P.H., Yu, L.Y., Huang, J.B., Yang, M.H., Lin, Y.Y.: Deep semantic matching with foreground detection and cycle-consistency. In: Asian Conference on Computer Vision. pp. 347–362. Springer (2018)
2018
Cited alongside, same era.
Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8971–8980 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9729–9738 (2020)
2020
Later among the works it cites.
Henaff, O.: Data-efficient image recognition with contrastive predictive coding. In: International Conference on Machine Learning. pp. 4182–4192. PMLR (2020)
2020
Later among the works it cites.
Jabri, A., Owens, A., Efros, A.A.: Space-time correspondence as a contrastive random walk. Advances in Neural Information Processing Systems (2020)
2020
Later among the works it cites.
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Rocco, I., Arandjelović, R., Sivic, J.: End-to-end weakly-supervised semantic alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6917–6925 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8934–8943 (2018)
2018
Cited alongside, same era.
Vondrick, C., Shrivastava, A., Fathi, A., Guadarrama, S., Murphy, K.: Tracking emerges by colorizing videos. In: Proceedings of the European conference on computer vision (ECCV). pp. 391–408 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Zhou, Q., Liang, X., Gong, K., Lin, L.: Adaptive temporal encoding network for video instance-level human parsing. In: Proceedings of the 26th ACM international conference on Multimedia. pp. 1527–1535 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Lai, Z., Lu, E., Xie, W.: Mast: A memory-augmented self-supervised tracker. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6479–6488 (2020)
2020
Later among the works it cites.
Liu, Y., Zhu, L., Yamada, M., Yang, Y.: Semantic correspondence as an optimal transport problem. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4463–4472 (2020)
2020
Later among the works it cites.
Min, J., Lee, J., Ponce, J., Cho, M.: Learning to compose hypercolumns for visual correspondence. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16. pp. 346–363. Springer (2020)
2020
Later among the works it cites.
Misra, I., Maaten, L.v.d.: Self-supervised learning of pretext-invariant representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6707–6717 (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: European conference on computer vision. pp. 402–419. Springer (2020)
2020
Later among the works it cites.
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. pp. 776–794. Springer (2020)
2020
Later among the works it cites.
Truong, P., Danelljan, M., Timofte, R.: Glu-net: Global-local universal network for dense flow and correspondences. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6258–6268 (2020)
2020
Later among the works it cites.
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., Proesmans, M., Van Gool, L.: Scan: Learning to classify images without labels. In: European Conference on Computer Vision. pp. 268–285. Springer (2020)
2020
Later among the works it cites.
Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., Hayhoe, M.: Human gaze assisted artificial intelligence: a review. In: IJCAI: Proceedings of the Conference. vol. 2020, p. 4951. NIH Public Access (2020)
2020
Later among the works it cites.
Ballard, D.H., Zhang, R.: The hierarchical evolution in human vision modeling. Topics in Cognitive Science 13
2021
Later among the works it cites.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9650–9660 (2021)
2021
Later among the works it cites.
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Min, J., Cho, M.: Convolutional hough matching networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2940–2950 (2021)
2021
Later among the works it cites.
Selvaraju, R.R., Desai, K., Johnson, J., Naik, N.: Casting your model: Learning to localize improves self-supervised representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11058–11067 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3024–3033 (2021)
2021
Later among the works it cites.
Xie, E., Ding, J., Wang, W., Zhan, X., Xu, H., Sun, P., Li, Z., Luo, P.: Detco: Unsupervised contrastive learning for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8392–8401 (2021)
2021
Later among the works it cites.
Xie, Z., Lin, Y., Zhang, Z., Cao, Y., Lin, S., Hu, H.: Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16684–16693 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.