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We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
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Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce · 2015
Earlier work this paper cites.
Proposal flow
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2016
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Deep residual learning for image recognition
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Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
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Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Proposal flow: Semantic correspondences from object proposals
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2017
Earlier work this paper cites.
Scnet: Learning semantic correspondence
Kai Han, Rafael S Rezende, Bumsub Ham, Kwan-Yee K Wong, Minsu Cho, Cordelia Schmid, and Jean Ponce · 2017
Earlier work this paper cites.
Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2018
Earlier work this paper cites.
Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
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D2-net: A trainable cnn for joint description and detection of local features
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler · 2019
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Dynamic context correspondence network for semantic alignment
Shuaiyi Huang, Qiuyue Wang, Songyang Zhang, Shipeng Yan, and Xuming He · 2019
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Sfnet: Learning object-aware semantic correspondence
Junghyup Lee, Dohyung Kim, Jean Ponce, and Bumsub Ham · 2019
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Hyperpixel flow: Semantic correspondence with multi-layer neural features
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2019
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Spair-71k: A large-scale benchmark for semantic correspondence
Deep vit features as dense visual descriptors
Shir Amir, Yossi Gandelsman, Shai Bagon, and Tali Dekel · 2021
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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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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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Later among the works it cites.
Patchmatch-based neighborhood consensus for semantic correspondence
Jae Yong Lee, Joseph DeGol, Victor Fragoso, and Sudipta N Sinha · 2021
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Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Temperature check: theory and practice for training models with softmax-cross-entropy losses
Atish Agarwala, Jeffrey Pennington, Yann Dauphin, and Sam Schoenholz · 2020
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Learning semantic correspondence exploiting an object-level prior
Junghyup Lee, Dohyung Kim, Wonkyung Lee, Jean Ponce, and Bumsub Ham · 2020
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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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Dual-resolution correspondence networks
Xinghui Li, Kai Han, Shuda Li, 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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Convolutional hough matching networks
Juhong Min 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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𝕏 \mathbb{X} resolution correspondence networks
Georgi Tinchev, Shuda Li, Kai Han, David Mitchell, and Rigas Kouskouridas · 2021
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Multi-scale matching networks for semantic correspondence
Dongyang Zhao, Ziyang Song, Zhenghao Ji, Gangming Zhao, Weifeng Ge, and Yizhou Yu · 2021
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Learning semantic correspondence with sparse annotations
Shuaiyi Huang, Luyu Yang, Bo He, Songyang Zhang, Xuming He, and Abhinav Shrivastava · 2022
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Semi-supervised learning of semantic correspondence with pseudo-labels
Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, and Seungryong Kim · 2022
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Transformatcher: Match-to-match attention for semantic correspondence
Seungwook Kim, Juhong Min, and Minsu Cho · 2022
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Probabilistic warp consistency for weakly-supervised semantic correspondences
Prune Truong, Martin Danelljan, Fisher Yu, and Luc Van Gool · 2022
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2022
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