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To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance.
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Claude E. Shannon · 1948
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Information, prediction, and query by committee
Yoav Freund, H. Sebastian Seung, Eli Shamir, and Naftali Tishby · 1992
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Information-based objective functions for active data selection
David J. C. MacKay · 1992
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Heterogeneous uncertainty sampling for supervised learning
David D. Lewis and Jason Catlett · 1994
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Detecting change in data streams
Daniel Kifer, Shai Ben-David, and Johannes Gehrke · 2004
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Active learning using pre-clustering
Hieu Tat Nguyen and Arnold W. M. Smeulders · 2004
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2007
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Multi-class active learning for image classification
Ajay J. Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Active instance sampling via matrix partition
Yuhong Guo · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, and Máté Lengyel · 2011
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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A convex optimization framework for active learning
Ehsan Elhamifar, Guillermo Sapiro, Allen Y. Yang, and S. Shankar Sastry · 2013
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Hierarchical subquery evaluation for active learning on a graph
Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz, and Gabriel J. Brostow · 2014
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Selecting influential examples: Active learning with expected model output changes
Alexander Freytag, Erik Rodner, and Joachim Denzler · 2014
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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Context aware active learning of activity recognition models
Mahmudul Hasan and Amit K. Roy-Chowdhury · 2015
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SUN RGB-D: A RGB-D scene understanding benchmark suite
Shuran Song, Samuel P. Lichtenberg, and Jianxiong Xiao · 2015
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Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G. Hauptmann · 2015
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Active and semi-supervised learning in ASR: benefits on the acoustic and language models
Thomas Drugman, Janne Pylkkönen, and Reinhard Kneser · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Active and semi-supervised learning for object detection with imperfect data
Phill-Kyu Rhee, Enkhbayar Erdenee, Shin Dong Kyun, Minhaz Uddin Ahmed, and SongGuo Jin · 2017
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Cost-effective active learning for deep image classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2017
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Edge and corner detection for unorganized 3d point clouds with application to robotic welding
Syeda Mariam Ahmed, Yan Zhi Tan, Chee-Meng Chew, Abdullah Al Mamun, and Fook Seng Wong · 2018
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The power of ensembles for active learning in image classification
William H. Beluch, Tim Genewein, Andreas Nürnberger, and Jan M. Köhler · 2018
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Localization-aware active learning for object detection
Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, and Ming-Yu Liu · 2018
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Lipschitz regularized deep neural networks converge and generalize
Adam M. Oberman and Jeff Calder · 2018
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Deep active learning for object detection
Soumya Roy, Asim Unmesh, and Vinay P. Namboodiri · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Active learning for deep detection neural networks
Hamed H. Aghdam, Abel Gonzalez-Garcia, Joost van de Weijer, and Antonio M. Lopez · 2019
Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov · 2020
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Infofocus: 3d object detection for autonomous driving with dynamic information modeling
Jun Wang, Shiyi Lan, Mingfei Gao, and Larry S. Davis · 2020
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3dssd: Point-based 3d single stage object detector
Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia · 2020
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State-relabeling adversarial active learning
Beichen Zhang, Liang Li, Shijie Yang, Shuhui Wang, Zheng-Jun Zha, and Qingming Huang · 2020
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Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks
Ahmed Al-Saffar, Alina Bialkowski, Mahsa Baktashmotlagh, Adnan Trakic, Lei Guo, and Amin M. Abbosh · 2021
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Deep active learning for efficient training of a lidar 3d object detector
Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, and Klaus Dietmayer · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
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Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, and José Miguel Hernández-Lobato · 2019
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Rapid performance gain through active model reuse
Feng Shi and Yu-Feng Li · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Integrating bayesian and discriminative sparse kernel machines for multi-class active learning
Weishi Shi and Qi Yu · 2019
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PAL : Pretext-based active learning
Shubhang Bhatnagar, Sachin Goyal, Darshan Tank, and Amit Sethi · 2021
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Sequential graph convolutional network for active learning
Razvan Caramalau, Binod Bhattarai, and Tae-Kyun Kim · 2021
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Active learning for deep object detection via probabilistic modeling
Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clément Farabet, and Jose M. Alvarez · 2021
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Batch active learning at scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, and Sanjiv Kumar · 2021
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Revisiting 3d object detection from an egocentric perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas A. Funkhouser, Yin Zhou, and Dragomir Anguelov · 2021
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Contrastive coding for active learning under class distribution mismatch
Pan Du, Suyun Zhao, Hui Chen, Shuwen Chai, Hong Chen, and Cuiping Li · 2021
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Semi-supervised active learning for semi-supervised models: Exploit adversarial examples with graph-based virtual labels
Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, and Yueting Zhuang · 2021
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Influence selection for active learning
Zhuoming Liu, Hao Ding, Huaping Zhong, Weijia Li, Jifeng Dai, and Conghui He · 2021
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Active contrastive learning of audio-visual video representations
Shuang Ma, Zhaoyang Zeng, Daniel McDuff, and Yale Song · 2021
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A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, and Xin Wang · 2021
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Label-efficient point cloud semantic segmentation: An active learning approach
Xian Shi, Xun Xu, Ke Chen, Lile Cai, Chuan Sheng Foo, and Kui Jia · 2021
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Qbox: Partial transfer learning with active querying for object detection
Ying-Peng Tang, Xiu-Shen Wei, Borui Zhao, and Sheng-Jun Huang · 2021
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Redal: Region-based and diversity-aware active learning for point cloud semantic segmentation
Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, and Winston H. Hsu · 2021
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Multiple instance active learning for object detection
Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, and Qixiang Ye · 2021
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Cartography active learning
Mike Zhang and Barbara Plank · 2021
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Not all labels are equal: Rationalizing the labeling costs for training object detection
Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, and Jose M Alvarez · 2022
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Active learning by feature mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Gholamreza (Reza) Haffari, Anton van den Hengel, and Javen Qinfeng Shi · 2022
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Active learning for point cloud semantic segmentation via spatial-structural diversity reasoning
Feifei Shao, Yawei Luo, Ping Liu, Jie Chen, Yi Yang, Yulei Lu, and Jun Xiao · 2022
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Active learning strategies for weakly-supervised object detection
Huy V Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, and Jean Ponce · 2022
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Weakly supervised object detection based on active learning
Xiao Wang, Xiang Xiang, Baochang Zhang, Xuhui Liu, Jianying Zheng, and QingLei Hu · 2022
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Entropy-based active learning for object detection with progressive diversity constraint
Jiaxi Wu, Jiaxin Chen, and Di Huang · 2022
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Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, and Yulan Guo · 2022
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