【CVPR2019】 教程 Tutorials List

CVPR2019 TUTORIALS 已经公布，各领域的教程总结如下

TUTORIALS

Visual Recognition and Beyond
Christoph Feichenhofer,Kaiming He, Ross Girshick, Georgia Gkioxari, Alexander Kirillov, and Piotr Dollar
📩 Christoph Feichenhofer


Towards Relightable Volumetric Performance Capture of Humans
Sean Fanello, Christoph Rhemann, Graham Fyffe, Jonathan Taylor, Sofien Bouaziz, Paul Debevec, Shahram Izadi
📩 Sean Fanello


Map Synchronization: from Object Correspondences to Neural Networks
Qixing Huang, Xiaowei Zhou , Junyan Zhu and Tinghui Zhou
📩 Qixing Huang


Camera based Physiological Measurement
Wenjin Wang , Sveta Zinger and Gerard de Haan
📩 Wenjin Wang


The Art of Solving Minimal Problems in Computer Vision
Zuzana Kukelova , Tomas Pajdla , Viktor Larsson, Magnus Oskarsson, Kalle Åström and Janne Heikkilä
📩 Zuzana Kukelova


arget="_blank">Learning Representations via Graph-structured Networks
Sifei Liu, Varun Jampani, Xiaolong Wang , Dhruv Batra, Abhinav Gupta, Jan Kautz, Ming-Hsuan Yang
📩 Sifei Liu


Deep Learning for Content Creation
Deqing Sun, Ming-Yu Liu, Orazio Gallo and Jan Kautz
📩 Deqing Sun


Capsule Networks for Computer Vision
Yogesh Singh Rawat, Mubarak Shah, Ulas Bagci, Sara Sabour, Rodney LaLonde, Kevin Duarte
📩 Yogesh Singh Rawat


Perception at Magic Leap
Ashwin Swaminathan , Jean-Yves Bouguet, Dan Farmer and David Molyneaux
📩 Ashwin Swaminathan


Tutorial on Action Classification and Video Modeling
Efstratios Gavves, Joao Carreira, Christoph Feichtenhofer, Lorenzo Torresani, Basura Fernando
📩 Efstratios Gavves


Distributed Private Machine Learning for Computer Vision: Federated Learning and Beyond
Brendan McMahan , Ramesh Raskar, Jakub Konečný, Otkrist Gupta, Hassan Takabi and Praneeth Vepakomma
📩 Brendan McMahan


Recent Advances in Visual Data Summarization
Rameswar Panda, Ehsan Elhamifar, Amin Karbasi and Michael Gygli
📩 Rameswar Panda


Vision meets mapping: computer vision for location-based reasoning and mapping
Xiang Ma
📩 Xiang Ma


Textures, Objects, Scenes: From Handcrafted Features to CNNs and Beyond
Li Liu, Bolei Zhou, Liang Zheng, and Wanli Ouyang
📩 Li Liu


Data-Driven Computational Imaging
Ashok Veeraraghavan , Guy Satat, Ramesh Raskar, Tristan Swedish, Vivek Boominathan
📩 Ashok Veeraraghavan


Metalearning for Computer Vision
Nikhil Naik, Frank Hutter, Nitish Keskar, Ramesh Raskar, and Richard Socher
📩 Nikhil Naik


Apollo: Open Autonomous Driving Platform
Tae Eun Choe, Liang Wang, Shiyu Song, Jiangtao Hu, Ruigang Yang,Jaewon Jung
📩 Tae Eun Choe


Tutorial on Visual Recognition of Families In the Wild
Joseph Robinson, Ming Shao and Yun Fu
📩 Joseph Robinson


Learning-based depth estimation from stereo and monocular images: successes, limitations and future challenges
Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai and Stefano Mattoccia
📩 Matteo Poggi


Perception, Prediction, and Large Scale Data Collection for Autonomous Cars
Luc Vincent, Peter Ondruska, Ashesh Jain, Sammy Omari and Vinay Shet
📩 Luc Vincent


OpenCV 4.x and more new tools for CV R&D
Alexander Bovyrin, Vadim Pisarevsky, and Nikita Manovich
📩 Alexander Bovyrin


Deep Reinforcement Learning for Computer Vision
Jiwen Lu, Liangliang Ren and Yongming Rao
📩 Jiwen Lu


Unifying Human Activity Understanding
Gunnar Sigurdsson and Michael Ryoo
📩 Gunnar Sigurdsson


Learning Cause-and-Effect in a Tensor Framework
M. Alex O. Vasilescu, Jean Kosaffi, Lieven DeLathauwer
📩 M. Alex O. Vasilescu


Bringing Robots to the Computer Vision Community
Adithyavairavan Murali , Dhiraj Gandhi, Lerrel Pinto, Deepak Pathak and Saurabh Gupta
📩 Adithyavairavan Murali

ref:http://cvpr2019.thecvf.com/program/tutorials

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