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A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes.
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia. 2019 · 1907
Earlier work this paper cites.
Transferable Semi-Supervised 3D Object Detection From RGB-D Data. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . 1931–1940
Yew Siang Tang and Gim Hee Lee. 2019 · 1940
Earlier work this paper cites.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles. 1981 · 1981
Earlier work this paper cites.
Image inpainting. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques . 417–424
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. 2000 · 2000
Earlier work this paper cites.
The Pascal Visual Object Classes (VOC) Challenge
Mark Everingham, Luc Gool, Christopher K. Williams, John Winn, and Andrew Zisserman. 2010 · 2010
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite. In Computer Vision and Pattern Recognition (CVPR) . IEEE, 3354–3361
Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012 · 2012
Earlier work this paper cites.
Selective Search for Object Recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders. 2013 · 2013
Earlier work this paper cites.
Visualizing and Understanding Convolutional Networks. In European Conference on Computer Vision . 818–833
D Zeiler Matthew and Fergus Rob. 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Beyond PASCAL: A Benchmark for 3D Object Detection in the Wild. In IEEE Winter Conference on Applications of Computer Vision (WACV)
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese. 2014 · 2014
Earlier work this paper cites.
3d object proposals for accurate object class detection. In Advances in Neural Information Processing Systems . 424–432
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun. 2015 · 2015
Earlier work this paper cites.
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, and Jean Ponce. 2015 · 2015
Earlier work this paper cites.
Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning
Junwei Han, Dingwen Zhang, Gong Cheng, Lei Guo, and Jinchang Ren. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Geoffrey E Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In International Conference for Learning Representations
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning. In 12th { \{ USENIX } \} Symposium on Operating Systems Design and Implementation ( { \{ OSDI } \} 16) . 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Weakly Supervised Deep Detection Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Hakan Bilen and Andrea Vedaldi. 2016 · 2016
Cited alongside, same era.
Monocular 3d object detection for autonomous driving. In Conference on Computer Vision and Pattern Recognition (CVPR) . 2147–2156
Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun. 2016 · 2016
Cited alongside, same era.
Cross modal distillation for supervision transfer. In IEEE conference on computer vision and pattern recognition (CVPR) . 2827–2836
Saurabh Gupta, Judy Hoffman, and Jitendra Malik. 2016 · 2016
Cited alongside, same era.
Deep Ordinal Regression Network for Monocular Depth Estimation. In Computer Vision and Pattern Recognition (CVPR)
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao. 2018 · 2018
Later among the works it cites.
Modality distillation with multiple stream networks for action recognition. In Proceedings of the European Conference on Computer Vision (ECCV) . 103–118
Nuno C Garcia, Pietro Morerio, and Vittorio Murino. 2018 · 2018
Later among the works it cites.
Joint Monocular 3D Vehicle Detection and Tracking
Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, and Fisher Yu. 2018 · 2018
Later among the works it cites.
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2018 · 2018
Later among the works it cites.
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ContextLocNet: Context-aware Deep Network Models for Weakly Supervised Localization. In Proc. European Conference on Computer Vision (ECCV), 2016
V. Kantorov, M. Oquab, Cho M., and I. Laptev. 2016 · 2016
Cited alongside, same era.
Weighted Unsupervised Learning for 3D Object Detection
Kamran Kowsari and Manal H Alassaf. 2016 · 2016
Cited alongside, same era.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu, Forrest N. Iandola, Peter H. Jin, and Kurt Keutzer. 2016 · 2016
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving. In IEEE CVPR , Vol. 1. 3
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia. 2017 · 2017
Cited alongside, same era.
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017 · 2017
Cited alongside, same era.
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
Zehao Huang and Naiyan Wang. 2017 · 2017
Cited alongside, same era.
3d bounding box estimation using deep learning and geometry. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 5632–5640
Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Košecká. 2017 · 2017
Cited alongside, same era.
Frustum PointNets for 3D Object Detection from RGB-D Data
Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas. 2017 · 2017
Cited alongside, same era.
Thomas Roddick, Alex Kendall, and Roberto Cipolla. 2018 · 2018
Later among the works it cites.
Pcl: Proposal cluster learning for weakly supervised object detection
Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, and Alan Loddon Yuille. 2018 · 2018
Later among the works it cites.
Min-Entropy Latent Model for Weakly Supervised Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1297–1306
Fang Wan, Pengxu Wei, Jianbin Jiao, Zhenjun Han, and Qixiang Ye. 2018 · 2018
Later among the works it cites.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou and Oncel Tuzel. 2018 · 2018
Later among the works it cites.
Variational Information Distillation for Knowledge Transfer
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai. 2019 · 2019
Later among the works it cites.
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
Zengyi Qin, Jinglu Wang, and Yan Lu. 2019a · 2019
Later among the works it cites.
Self paced deep learning for weakly supervised object detection
Enver Sangineto, Moin Nabi, Dubravko Culibrk, and Nicu Sebe. 2019 · 2019
Later among the works it cites.
A Novel Weakly-Supervised Approach for RGB-D-Based Nuclear Waste Object Detection
Li Sun, Cheng Zhao, Zhi Yan, Pengcheng Liu, Tom Duckett, and Rustam Stolkin. 2019 · 2019
Later among the works it cites.
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. In CVPR
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Weinberger. 2019 · 2019
Later among the works it cites.
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
Xinshuo Weng and Kris Makoto Kitani. 2019 · 2019
Later among the works it cites.
Object Instance Mining for Weakly Supervised Object Detection
Chenhao Lin, Siwen Wang, Dongqi Xu, Yu Lu, and Wayne Zhang. 2020 · 2020
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