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Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman and Others · 1960
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Constrained k-means clustering with background knowledge
Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schrödl · 2001
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Vehicle trajectory prediction based on motion model and maneuver recognition
Adam Houenou, Philippe Bonnifait, Véronique Cherfaoui, and Wen Yao · 2013
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 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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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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Learning features by watching objects move
Deepak Pathak, Ross B. Girshick, Piotr Dollár, Trevor Darrell, and Bharath Hariharan · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Intentnet: Learning to predict intention from raw sensor data
Sergio Casas, Wenjie Luo, and Raquel Urtasun · 2018
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Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms
Nachiket Deo and Mohan M. Trivedi · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models
Guotao Xie, Hongbo Gao, Lijun Qian, Bin Huang, Keqiang Li, and Jianqiang Wang · 2018
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Mayank Bansal, Alex Krizhevsky, and Abhijit S. Ogale · 2019
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Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov · 2019
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Argoverse: 3d tracking and forecasting with rich maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays · 2019
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TNT: target-driven trajectory prediction
Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, Congcong Li, and Dragomir Anguelov · 2020
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Self-supervised simultaneous multi-step prediction of road dynamics and cost map
Elmira Amirloo Abolfathi, Mohsen Rohani, Ershad Banijamali, Jun Luo, and Pascal Poupart · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Large scale interactive motion forecasting for autonomous driving : The waymo open motion dataset
Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R. Qi, Yin Zhou, Zoey Yang, Aurelien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, and Dragomir Anguelov · 2021
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HOME: heatmap output for future motion estimation
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, and Fabien Moutarde · 2021
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton · 2019
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End-to-end interpretable neural motion planner
Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, and Raquel Urtasun · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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Vectornet: Encoding HD maps and agent dynamics from vectorized representation
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec · 2020
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Self-supervised auxiliary learning with meta-paths for heterogeneous graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, and Hyunwoo J. Kim · 2020
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Self-supervised learning on graphs: Deep insights and new direction
Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang · 2020
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Densetnt: End-to-end trajectory prediction from dense goal sets
Junru Gu, Chen Sun, and Hang Zhao · 2021
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Multi-modal motion prediction with transformer-based neural network for autonomous driving
Zhiyu Huang, Xiaoyu Mo, and Chen Lv · 2021
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Self-supervised learning is more robust to dataset imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, and Tengyu Ma · 2021
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Graph self-supervised learning: A survey
Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S. Yu · 2021
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Multimodal motion prediction with stacked transformers
Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, and Bolei Zhou · 2021
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Scene transformer: A unified multi-task model for behavior prediction and planning
Jiquan Ngiam, Benjamin Caine, Vijay Vasudevan, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David Weiss, Benjamin Sapp, Zhifeng Chen, and Jonathon Shlens · 2021
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Learning to predict vehicle trajectories with model-based planning
Haoran Song, Di Luan, Wenchao Ding, Michael Yu Wang, and Qifeng Chen · 2021
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Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Khaled S. Refaat, Nigamaa Nayakanti, Andre Cornman, Kan Chen, Bertrand Douillard, Chi-Pang Lam, Dragomir Anguelov, and Benjamin Sapp · 2021
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TPCN: temporal point cloud networks for motion forecasting
Maosheng Ye, Tongyi Cao, and Qifeng Chen · 2021
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Lanercnn: Distributed representations for graph-centric motion forecasting
Wenyuan Zeng, Ming Liang, Renjie Liao, and Raquel Urtasun · 2021
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Latent variable sequential set transformers for joint multi-agent motion prediction
Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D’Souza, Samira E. Kahou, Felix Heide, and Christopher Pal · 2022
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DCMS: motion forecasting with dual consistency and multi-pseudo-target supervision
Maosheng Ye, Jiamiao Xu, Xunnong Xu, Tongyi Cao, and Qifeng Chen · 2022
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