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The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term.
Determining optical flow
Berthold K.P. Horn and Brian G. Schunck · 1981
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An iterative image registration technique with an application to stereo vision
Bruce D. Lucas and Takeo Kanade · 1981
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Exact maximum a posteriori estimation for binary images
Dorothy M Greig, Bruce T Porteous, and Allan H Seheult · 1989
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Probability distributions of optical flow
Eero P Simoncelli, Edward H Adelson, and David J Heeger · 1991
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Robust dense matching using local and global geometric constraints
Maxime Lhuillier and Long Quan · 2000
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Bayes’ theorem
James Joyce · 2003
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Visual odometry
David Nistér, Oleg Naroditsky, and James Bergen · 2004
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Robust active shape models: A robust, generic and simple automatic segmentation tool
Julien Abi-Nahed, Marie-Pierre Jolly, and Guang-Zhong Yang · 2006
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Simultaneous localization and mapping (slam): Part ii
Tim Bailey and Hugh Durrant-Whyte · 2006
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A confidence measure for variational optic flow methods
Andrés Bruhn and Joachim Weickert · 2006
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Simultaneous localization and mapping: part i
Hugh Durrant-Whyte and Tim Bailey · 2006
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An adaptive confidence measure for optical flows based on linear subspace projections
Claudia Kondermann, Daniel Kondermann, Bernd Jähne, and Christoph Garbe · 2007
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A statistical confidence measure for optical flows
Claudia Kondermann, Rudolf Mester, and Christoph S Garbe · 2008
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Learning optical flow
Deqing Sun, Stefan Roth, John P Lewis, and Michael J Black · 2008
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Patchmatch: A randomized correspondence algorithm for structural image editing
Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman · 2009
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Large displacement optical flow: descriptor matching in variational motion estimation
Thomas Brox and Jitendra Malik · 2010
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Brief: Binary robust independent elementary features
Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua · 2010
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Repfinder: finding approximately repeated scene elements for image editing
Ming-Ming Cheng, Fang-Lue Zhang, Niloy J Mitra, Xiaolei Huang, and Shi-Min Hu · 2010
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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Secrets of optical flow estimation and their principles
Deqing Sun, Stefan Roth, and Michael J Black · 2010
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Motion estimation with non-local total variation regularization
Manuel Werlberger, Thomas Pock, and Horst Bischof · 2010
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Total variation regularization of local-global optical flow
Marius Drulea and Sergiu Nedevschi · 2011
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A graph-matching kernel for object categorization
Olivier Duchenne, Armand Joulin, and Jean Ponce · 2011
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Bootstrap optical flow confidence and uncertainty measure
Jan Kybic and Claudia Nieuwenhuis · 2011
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Learning a confidence measure for optical flow
Oisin Mac Aodha, Ahmad Humayun, Marc Pollefeys, and Gabriel J Brostow · 2012
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Deformable spatial pyramid matching for fast dense correspondences
Jaechul Kim, Ce Liu, Fei Sha, and Kristen Grauman · 2013
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Tv-l1 optical flow estimation
Javier Sánchez Pérez, Enric Meinhardt-Llopis, and Gabriele Facciolo · 2013
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Deepflow: Large displacement optical flow with deep matching
Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid · 2013
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Return of the devil in the details: Delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
Cited alongside, same era.
Epicflow: Edge-preserving interpolation of correspondences for optical flow
Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, and Cordelia Schmid · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
Monocular 3d object detection for autonomous driving
Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2016
Cited alongside, same era.
Ncnet: Neighbourhood consensus networks for estimating image correspondences
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Cross-domain correspondence learning for exemplar-based image translation
Pan Zhang, Bo Zhang, Dong Chen, Lu Yuan, and Fang Wen · 2020
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Conditional image generation with score-based diffusion models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, and Christian Etmann · 2021
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
Proposal flow
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2016
Cited alongside, same era.
Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
Cited alongside, same era.
Joint recovery of dense correspondence and cosegmentation in two images
Tatsunori Taniai, Sudipta N Sinha, and Yoichi Sato · 2016
Cited alongside, same era.
Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
Cited alongside, same era.
Dslr-quality photos on mobile devices with deep convolutional networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
Cats: Cost aggregation transformers for visual correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, and Seungryong Kim · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Deep matching prior: Test-time optimization for dense correspondence
Sunghwan Hong and Seungryong Kim · 2021
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Cotr: Correspondence transformer for matching across images
Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, and Kwang Moo Yi · 2021
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Patchmatch-based neighborhood consensus for semantic correspondence
Jae Yong Lee, Joseph DeGol, Victor Fragoso, and Sudipta N Sinha · 2021
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Convolutional hough matching networks
Juhong Min and Minsu Cho · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Learning accurate dense correspondences and when to trust them
Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2021
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ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al · 2022
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Cats++: Boosting cost aggregation with convolutions and transformers
Seokju Cho, Sunghwan Hong, and Seungryong Kim · 2022
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Giorgio Giannone, Didrik Nielsen, and Ole Winther · 2022
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Diffusioninst: Diffusion model for instance segmentation
Zhangxuan Gu, Haoxing Chen, Zhuoer Xu, Jun Lan, Changhua Meng, and Weiqiang Wang · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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Diffpose: Multi-hypothesis human pose estimation using diffusion models
Karl Holmquist and Bastian Wandt · 2022
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Dag: Depth-aware guidance with denoising diffusion probabilistic models
Gyeongnyeon Kim, Wooseok Jang, Gyuseong Lee, Susung Hong, Junyoung Seo, and Seungryong Kim · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Pyramidal denoising diffusion probabilistic models
Dohoon Ryu and Jong Chul Ye · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano · 2022
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Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2023
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Diffusiondepth: Diffusion denoising approach for monocular depth estimation
Yiqun Duan, Xianda Guo, and Zheng Zhu · 2023
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Dkm: Dense kernelized feature matching for geometry estimation
Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, and Michael Felsberg · 2023
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Ddp: Diffusion model for dense visual prediction
Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, and Ping Luo · 2023
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Midms: Matching interleaved diffusion models for exemplar-based image translation
Junyoung Seo, Gyuseong Lee, Seokju Cho, Jiyoung Lee, and Seungryong Kim · 2023
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Pdc-net+: Enhanced probabilistic dense correspondence network
Prune Truong, Martin Danelljan, Radu Timofte, and Luc Van Gool · 2023
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