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Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
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Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele · 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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Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim · 2014
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CNN features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Fabrice Robinet, Rémi Arnaud, Tony Parisi, and Patrick Cozzi · 2014
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Touchcut: Fast image and video segmentation using single-touch interaction
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Ali Borji, Ming-Ming Cheng, Huaizu Jiang, and Jia Li · 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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Flownet: Learning optical flow with convolutional networks
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A deeper look at dataset bias, 2015
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2015
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E Coumans and Y Bai · 2016
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Pybullet, a python module for physics simulation for games, robotics and machine learning
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Virtual worlds as proxy for multi-object tracking analysis
A Gaidon, Q Wang, Y Cabon, and E Vig · 2016
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Synthetic data for text localisation in natural images
Ankush Gupta, Andrea Vedaldi, and Andrew Zisserman · 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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Volumetric hierarchical approximate convex decomposition
Khaled Mamou, E Lengyel, and AK Peters · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N Mayer, E Ilg, P Hausser, P Fischer, and others · 2016
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UnrealCV: Connecting computer vision to unreal engine
W Qiu and A Yuille · 2016
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Playing for data: Ground truth from computer games
S R Richter, V Vineet, S Roth, and V Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G Ros, L Sellart, J Materzynska, and others · 2016
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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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Structure-measure: A new way to evaluate foreground maps
Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji · 2017
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Submanifold sparse convolutional networks
Benjamin Graham and Laurens van der Maaten · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Scenenet RGB-D: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?
J McCormac, A Handa, and others · 2017
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The 2017 davis challenge on video object segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, and Luc Van Gool · 2017
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases, 2020
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Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R Zaiane, and Martin Jagersand · 2020
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Max Schwarz and Sven Behnke · 2020
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Bylabel: A boundary based semi-automatic image annotation tool
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PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume
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NDDS: NVIDIA deep learning dataset synthesizer, 2018
Thang To, Jonathan Tremblay, Duncan McKay, Yukie Yamaguchi, Kirby Leung, Adrian Balanon, Jia Cheng, William Hodge, and Stan Birchfield · 2018
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Deep leaf segmentation using synthetic data
Daniel Ward, Peyman Moghadam, and Nicolas Hudson · 2018
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Gibson env: Real-world perception for embodied agents
F Xia, A R Zamir, Z He, A Sax, J Malik, and others · 2018
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Why do deep convolutional networks generalize so poorly to small image transformations?, 2019
Aharon Azulay and Yair Weiss · 2019
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MONet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
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RAFT: Recurrent all-pairs field transforms for optical flow
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Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments
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Towards fairer datasets: filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
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Simpose: Effectively learning densepose and surface normals of people from simulated data
Tyler Zhu, Per Karlsson, and Christoph Bregler · 2020
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Pass: An imagenet replacement for self-supervised pretraining without humans
Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, and Andrea Vedaldi · 2021
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Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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Blender - a 3D modelling and rendering package
Blender Online Community · 2021
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Unity perception: Generate synthetic data for computer vision, 2021
Steve Borkman, Adam Crespi, Saurav Dhakad, Sujoy Ganguly, Jonathan Hogins, You-Cyuan Jhang, Mohsen Kamalzadeh, Bowen Li, Steven Leal, Pete Parisi, Cesar Romero, Wesley Smith, Alex Thaman, Samuel Warren, and Nupur Yadav · 2021
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ABO: Dataset and benchmarks for Real-World 3D object understanding
Jasmine Collins, Shubham Goel, Achleshwar Luthra, Leon Xu, Kenan Deng, Xi Zhang, Tomas F Yago Vicente, Himanshu Arora, Thomas Dideriksen, Matthieu Guillaumin, and Jitendra Malik · 2021
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
A Eftekhar, A Sax, and others · 2021
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Blender 2.93.6 release candidate python api documentation, 2021
Blender Foundation · 2021
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Animeceleb: Large-scale animation celebfaces dataset via controllable 3d synthetic models, 2021
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Conditional Object-Centric Learning from Video
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Scanned objects dataset of common household objects, 2021
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Autoflow: Learning a bet @articleMayer2016-xo, title = ”A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation”, author = ”Mayer, N and Ilg, E and Hausser, P and Fischer, P and others”, journal = ”Proceedings of the”, publisher = ”openaccess.thecvf.com”, year = 2016 ter training set for optical flow
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