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We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Image alignment and stitching: A tutorial
Richard Szeliski · 2006
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Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
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Fast cost-volume filtering for visual correspondence and beyond
Asmaa Hosni, Christoph Rhemann, Michael Bleyer, Carsten Rother, and Margrit Gelautz · 2012
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Unsupervised joint object discovery and segmentation in internet images
Michael Rubinstein, Armand Joulin, Johannes Kopf, and Ce Liu · 2013
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Universal correspondence network
Christopher B Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Chandraker · 2016
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Proposal flow
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Joint recovery of dense correspondence and cosegmentation in two images
Tatsunori Taniai, Sudipta N Sinha, and Yoichi Sato · 2016
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Proposal flow: Semantic correspondences from object proposals
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2017
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Scnet: Learning semantic correspondence
Kai Han, Rafael S Rezende, Bumsub Ham, Kwan-Yee K Wong, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Fcss: Fully convolutional self-similarity for dense semantic correspondence
Seungryong Kim, Dongbo Min, Bumsub Ham, Sangryul Jeon, Stephen Lin, and Kwanghoon Sohn · 2017
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Visual attribute transfer through deep image analogy
Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2018
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Liteflownet: A lightweight convolutional neural network for optical flow estimation
Tak-Wai Hui, Xiaoou Tang, and Chen Change Loy · 2018
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Parn: Pyramidal affine regression networks for dense semantic correspondence
Sangryul Jeon, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn · 2018
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Correspondence networks with adaptive neighbourhood consensus
Shuda Li, Kai Han, Theo W Costain, Henry Howard-Jenkins, and Victor Prisacariu · 2020
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Semantic correspondence as an optimal transport problem
Yanbin Liu, Linchao Zhu, Makoto Yamada, and Yi Yang · 2020
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Learning to compose hypercolumns for visual correspondence
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2020
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Efficient neighbourhood consensus networks via submanifold sparse convolutions
Ignacio Rocco, Relja Arandjelović, 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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Recurrent transformer networks for semantic correspondence
Seungryong Kim, Stephen Lin, Sang Ryul Jeon, Dongbo Min, and Kwanghoon Sohn · 2018
Cited alongside, same era.
End-to-end weakly-supervised semantic alignment
Ignacio Rocco, Relja Arandjelović, and Josef Sivic · 2018
Cited alongside, same era.
Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
Cited alongside, same era.
Attentive semantic alignment with offset-aware correlation kernels
Paul Hongsuck Seo, Jongmin Lee, Deunsol Jung, Bohyung Han, and Minsu Cho · 2018
Cited alongside, same era.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
Cited alongside, same era.
Dynamic context correspondence network for semantic alignment
Shuaiyi Huang, Qiuyue Wang, Songyang Zhang, Shipeng Yan, and Xuming He · 2019
Cited alongside, same era.
Semantic attribute matching networks
Seungryong Kim, Dongbo Min, Somi Jeong, Sunok Kim, Sangryul Jeon, and Kwanghoon Sohn · 2019
Cited alongside, same era.
Peize Sun, Yi Jiang, Rufeng Zhang, Enze Xie, Jinkun Cao, Xinting Hu, Tao Kong, Zehuan Yuan, Changhu Wang, and Ping Luo · 2020
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Training data-efficient image transformers and distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Gocor: Bringing globally optimized correspondence volumes into your neural network
Prune Truong, Martin Danelljan, Luc V Gool, and Radu Timofte · 2020
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Glu-net: Global-local universal network for dense flow and correspondences
Prune Truong, Martin Danelljan, and Radu Timofte · 2020
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Disk: Learning local features with policy gradient
Michał J Tyszkiewicz, Pascal Fua, and Eduard Trulls · 2020
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Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 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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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Convolutional hough matching networks
Juhong Min and Minsu Cho · 2021
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Hypercorrelation squeeze for few-shot segmentation
Juhong Min, Dahyun Kang, and Minsu Cho · 2021
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Loftr: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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Going deeper with image transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou · 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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