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Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images.
An Iterative Image Registration Technique With an Application to Stereo Vision
Bruce D Lucas and Takeo Kanade · 1981
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Convolutional Networks for Images, Speech, and Time Series
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Shape context: A new descriptor for shape matching and object recognition
Serge Belongie, Jitendra Malik, and Jan Puzicha · 2000
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Two-View Geometry Estimation Unaffected by a Dominant Plane
Ondrej Chum, Tomas Werner, and Jiri Matas · 2005
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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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Building rome on a cloudless day
Jan-Michael Frahm, Pierre Fite-Georgel, David Gallup, Tim Johnson, Rahul Raguram, Changchang Wu, Yi-Hung Jen, Enrique Dunn, Brian Clipp, Svetlana Lazebnik, and Marc Pollefeys · 2010
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The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Building rome in a day
Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M. Seitz, and Rick Szeliski · 2011
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A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional neural network based image classification using small training sample size
Shuying Liu and Weihong Deng · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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Lift: Learned invariant feature transform
Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua · 2016
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Proposal flow
Bumsub Ham, Minsu Cho, Cordelia Schmid, and Jean Ponce · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz 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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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Learning to Find Good Correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua · 2018
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Neural best-buddies: Sparse cross-domain correspondence
Kfir Aberman, Jing Liao, Mingyi Shi, Dani Lischinski, Baoquan Chen, and Daniel Cohen-Or · 2018
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Spair-71k: A large-scale benchmark for semantic correspondence
Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho · 2019
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Normalized object coordinate space for category-level 6d object pose and size estimation
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J. Guibas · 2019
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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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Attentive context normalization for robust permutation-equivariant learning
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
Juhong Min, Seungwook Kim, and Minsu Cho · 2021
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Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, and Seungryong Kim · 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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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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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
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Weiwei Sun, Wei Jiang, Andrea Tagliasacchi, Eduard Trulls, and Kwang Moo Yi · 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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GOCor: Bringing globally optimized correspondence volumes into your neural network
Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
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Image matching from handcrafted to deep features: A survey
Jiayi Ma, Xingyu Jiang, Aoxiang Fan, Junjun Jiang, and Junchi Yan · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Colmap: A memory-efficient occupancy grid mapping framework
Alex Fisher, Ricardo Cannizzaro, Madeleine Cochrane, Chatura Nagahawatte, and Jennifer L Palmer · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Mvdecor: Multi-view dense correspondence learning for fine-grained 3d segmentation
Gopal Sharma, Kangxue Yin, Subhransu Maji, Evangelos Kalogerakis, Or Litany, and Sanja Fidler · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall · 2022
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Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, and Artem Babenko · 2022
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Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann · 2022
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Asic: Aligning sparse in-the-wild image collections
Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, and Abhishek Kar · 2023
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Gradient-free textual inversion
Zhengcong Fei, Mingyuan Fan, and Junshi Huang · 2023
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Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation
Anton Voronov, Mikhail Khoroshikh, Artem Babenko, and Max Ryabinin · 2023
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Asic: Aligning sparse in-the-wild image collections
Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, and Abhishek Kar · 2023
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DreamBooth: Fine Tuning Text-to-image Diffusion Models for Subject-Driven Generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Adding conditional control to text-to-image diffusion models, 2023
Lvmin Zhang and Maneesh Agrawala · 2023
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Diffusion hyperfeatures: Searching through time and space for semantic correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell · 2023
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A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa Polania Cabrera, Varun Jampani, Deqing Sun, and Ming-Hsuan Yang · 2023
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Emergent correspondence from image diffusion
Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan · 2023
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Slime: Segment like me
Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi, Ali Mahdavi Amiri, and Ghassan Hamarneh · 2023
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