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Passive methods for object detection and segmentation treat images of the same scene as individual samples and do not exploit object permanence across multiple views.
Active vision
John Aloimonos, Isaac Weiss, and Amit Bandyopadhyay · 1988
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Animate vision
Dana H. Ballard · 1991
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Orientation dependence in the recognition of familiar and novel views of three-dimensional objects
Shimon Edelman and Heinrich H Bülthoff · 1992
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Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects
Michael J. Tarr · 1995
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A fast marching level set method for monotonically advancing fronts
James A Sethian · 1996
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Contextual priming for object detection
Antonio Torralba · 2003
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Active learning literature survey
Burr Settles · 2009
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Efficient inference in fully connected crfs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun · 2011
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Large-scale live active learning: Training object detectors with crawled data and crowds
Sudheendra Vijayanarasimhan and Kristen Grauman · 2011
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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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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Learning to see by moving
Pulkit Agrawal, Joao Carreira, and Jitendra Malik · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Pairwise decomposition of image sequences for active multi-view recognition
Edward Johns, S. Leutenegger, and A. Davison · 2016
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Active sensing in the categorization of visual patterns
Scott Cheng-Hsin Yang, Mate Lengyel, and Daniel M Wolpert · 2016
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A dataset for developing and benchmarking active vision
Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Košecká, and Alexander C Berg · 2017
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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CARLA: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Debidatta Dwibedi, Ishan Misra, and Martial Hebert · 2017
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Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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The more you know: Using knowledge graphs for image classification
Kenneth Marino, Ruslan Salakhutdinov, and Abhinav Gupta · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles R. Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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3d scene graph: A structure for unified semantics, 3d space, and camera
Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese · 2019
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
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Scene memory transformer for embodied agents in long-horizon tasks
Kuan Fang, Alexander Toshev, Li Fei-Fei, and Silvio Savarese · 2019
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End-to-end policy learning for active visual categorization
Dinesh Jayaraman and Kristen Grauman · 2019
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Habitat: A Platform for Embodied AI Research
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Toddler-inspired visual object learning
Sven Bambach, David J. Crandall, Linda B. Smith, and Chen Yu · 2018
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Embodied question answering
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra · 2018
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Cost-sensitive active learning for intracranial hemorrhage detection
Weicheng Kuo, Christian Häne, E. Yuh, P. Mukherjee, and Jitendra Malik · 2018
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Pointcnn: Convolution on
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
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Learning instance segmentation by interaction
Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, and Jitendra Malik · 2018
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Frustum pointnets for 3d object detection from rgb-d data
Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas · 2018
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Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 2019
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The Replica dataset: A digital replica of indoor spaces
Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J. Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, Anton Clarkson, Mingfei Yan, Brian Budge, Yajie Yan, Xiaqing Pan, June Yon, Yuyang Zou, Kimberly Leon, Nigel Carter, Jesus Briales, Tyler Gillingham, Elias Mueggler, Luis Pesqueira, Manolis Savva, Dhruv Batra, Hauke M. Strasdat, Renzo De Nardi, Michael Goesele, Steven Lovegrove, and Richard Newcombe · 2019
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Ldls: 3-d object segmentation through label diffusion from 2-d images
Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, and Mark Campbell · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Embodied amodal recognition: Learning to move to perceive objects
J Yang, Z Ren, M Xu, X Chen, DJ Crandall, D Parikh, and D Batra · 2019
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Semantic curiosity for active visual learning
DS Chaplot, H Jiang, S Gupta, and A Gupta · 2020
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Learning to explore using active neural slam
Devendra Singh Chaplot, Dhiraj Gandhi, Saurabh Gupta, Abhinav Gupta, and Ruslan Salakhutdinov · 2020
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
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Tracking emerges by looking around static scenes, with neural 3d mapping
Adam W. Harley, Shrinidhi Kowshika Lakshmikanth, Paul Schydlo, and Katerina Fragkiadaki · 2020
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Deep learning for generic object detection: A survey
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jiechen, Xinwang Liu, and Matti Pietikäinen · 2020
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A survey on performance metrics for object-detection algorithms
R. Padilla, S. L. Netto, and E. A. B. da Silva · 2020
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
Senthil Purushwalkam and Abhinav Gupta · 2020
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Learning data augmentation strategies for object detection
Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, and Quoc V. Le · 2020
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