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Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity.
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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Learning depth from single monocular images
Ashutosh Saxena, Sung Chung, and Andrew Ng · 2005
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Occlusion-aware optical flow estimation
Serdar Ince and Janusz Konrad · 2008
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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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Make3D: Learning 3D scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y. Ng · 2009
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Motion detail preserving optical flow estimation
Li Xu, Jiaya Jia, and Yasuyuki Matsushita · 2011
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A naturalistic open source movie for optical flow evaluation
Daniel J. Butler, Jonas Wulff, Garrett B. Stanley, and Michael J. Black · 2012
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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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Indoor segmentation and support inference from RGBD images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Layered segmentation and optical flow estimation over time
Deqing Sun, Erik B. Sudderth, and Michael J. Black · 2012
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Vision meets Robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 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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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Inpainting of missing values in the kinect sensor’s depth maps based on background estimates
Martin Stommel, Michael Beetz, and Weiliang Xu · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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ShapeNet: An information-rich 3D model repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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FlowNet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Joint 3D estimation of vehicles and scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Unsupervised CNN for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar Bg, Gustavo Carneiro, and Ian Reid · 2016
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Understanding real world indoor scenes with synthetic data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan, Simon Stent, and Roberto Cipolla · 2016
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The HCI Benchmark Suite: Stereo and flow ground truth with uncertainties for urban autonomous driving
Daniel Kondermann, Rahul Nair, Katrin Honauer, Karsten Krispin, Jonas Andrulis, Alexander Brock, Burkhard Gussefeld, Mohsen Rahimimoghaddam, Sabine Hofmann, Claus Brenner, et al · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
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Professor Forcing: A new algorithm for training recurrent networks
Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, and Yoshua Bengio · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Yuanzhouhan Cao, Zifeng Wu, and Chunhua Shen · 2017
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ScanNet: Richly-annotated 3D reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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MirrorFlow: Exploiting symmetries in joint optical flow and occlusion estimation
Junhwa Hur and Stefan Roth · 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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SceneNet RGB-D: Can 5M synthetic images beat generic imagenet pre-training on indoor segmentation?
SynSin: End-to-end view synthesis from a single image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson · 2020
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AdaBins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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Infinite Nature: Perpetual view generation of natural scenes from a single image
Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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John McCormac, Ankur Handa, Stefan Leutenegger, and Andrew J. Davison · 2017
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Playing for benchmarks
Stephan R. Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
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SeqGAN: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Learning rigidity in dynamic scenes with a moving camera for 3D motion field estimation
Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, and Jan Kautz · 2018
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Object scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2018
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AutoFlow: Learning a better training set for optical flow
Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, and Ce Liu · 2021
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Separable flow: Learning motion cost volumes for optical flow estimation
Feihu Zhang, Oliver J. Woodford, Victor Adrian Prisacariu, and Philip H.S. Torr · 2021
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Transformer-based dual relation graph for multi-label image recognition
Jiawei Zhao, Ke Yan, Yifan Zhao, Xiaowei Guo, Feiyue Huang, and Jia Li · 2021
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alex Nichol · 2022
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Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti (Derek) Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, and Andrea Tagliasacchi · 2022
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FlowFormer: A transformer architecture for optical flow
Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2022
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Perceiver IO: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al · 2022
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Imposing consistency for optical flow estimation
Jisoo Jeong, Jamie Lin, Fatih Porikli, and Nojun Kwak · 2022
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BinsFormer: Revisiting adaptive bins for monocular depth estimation
Zhenyu Li, Xuyang Wang, Xianming Liu, and Junjun Jiang · 2022
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Learning optical flow with adaptive graph reasoning
Ao Luo, Fan Yang, Kunming Luo, Xin Li, Haoqiang Fan, and Shuaicheng Liu · 2022
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On distillation of guided diffusion models
Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 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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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Step-unrolled denoising autoencoders for text generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, and Aaron van den Oord · 2022
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CRAFT: Cross-attentional flow transformer for robust optical flow
Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu · 2022
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GMFlow: Learning optical flow via global matching
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao · 2022
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Attention Attention Everywhere: Monocular depth prediction with skip attention
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Imagen Editor and EditBench: Advancing and evaluating text-guided image inpainting
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Revealing the dark secrets of masked image modeling
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