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Learning a good representation for space-time correspondence is the key for various computer vision tasks, including tracking object bounding boxes and performing video object pixel segmentation.
Determining optical flow
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High accuracy optical flow estimation based on a theory for warping
Thomas Brox, Andrés Bruhn, Nils Papenberg, and Joachim Weickert · 2004
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Strike a pose: Tracking people by finding stylized poses
Deva Ramanan, David A Forsyth, and Andrew Zisserman · 2005
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Efficient mean-shift tracking via a new similarity measure
Changjiang Yang, Ramani Duraiswami, and Larry Davis · 2005
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Hao Wu, Aswin C Sankaranarayanan, and Rama Chellappa · 2007
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People-tracking-by-detection and people-detection-by-tracking
Mykhaylo Andriluka, Stefan Roth, and Bernt Schiele · 2008
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Large displacement optical flow
Thomas Brox, Christoph Bregler, and Jitendra Malik · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Recurrent tracking using multifold consistency
Pan Pan, Fatih Porikli, and Dan Schonfeld · 2009
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Forward-backward error: Automatic detection of tracking failures
Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas · 2010
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Secrets of optical flow estimation and their principles
Deqing Sun, Stefan Roth, and Michael J Black · 2010
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Sift flow: Dense correspondence across scenes and its applications
Ce Liu, Jenny Yuen, and Antonio Torralba · 2011
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A naturalistic open source movie for optical flow evaluation
Daniel J Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black · 2012
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Tracking-learning-detection
Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas · 2012
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Towards understanding action recognition
Hueihan Jhuang, Juergen Gall, Silvia Zuffi, Cordelia Schmid, and Michael J Black · 2013
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Learning a deep compact image representation for visual tracking
Naiyan Wang and Dit-Yan Yeung · 2013
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High-speed tracking with kernelized correlation filters
João F Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista · 2014
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A scale adaptive kernel correlation filter tracker with feature integration
Yang Li and Jianke Zhu · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Learning to see by moving
Pulkit Agrawal, Joao Carreira, and Jitendra Malik · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Unsupervised learning of spatiotemporally coherent metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun · 2015
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Learning image representations tied to ego-motion
Dinesh Jayaraman and Kristen Grauman · 2015
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Deep multi-scale video prediction beyond mean square error
Michaël Mathieu, Camille Couprie, and Yann LeCun · 2015
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Unsupervised learning of video representations using LSTMs
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Object tracking benchmark
Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang · 2015
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Adaptive temporal encoding network for video instance-level human parsing
Qixian Zhou, Xiaodan Liang, Ke Gong, and Liang Lin · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Tracking without bells and whistles
Philipp Bergmann, Tim Meinhardt, and Laura Leal-Taixe · 2019
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Got-10k: A large high-diversity benchmark for generic object tracking in the wild
Lianghua Huang, Xin Zhao, and Kaiqi Huang · 2019
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Self-supervised learning for video correspondence flow
Zihang Lai and Weidi Xie · 2019
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Fully-convolutional siamese networks for object tracking
Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr · 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 edges
Yin Li, Manohar Paluri, James M. Rehg, and Piotr Dollár · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, 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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Joint-task self-supervised learning for temporal correspondence
Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang · 2019
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Unsupervised deep tracking
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Learning correspondence from the cycle-consistency of time
Xiaolong Wang, Allan Jabri, and Alexei A Efros · 2019
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Unsupervised embedding learning via invariant and spreading instance feature
Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
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A simple framework for contrastive learning of visual representations
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Improved baselines with momentum contrastive learning
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Momentum contrast for unsupervised visual representation learning
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Space-time correspondence as a contrastive random walk
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Space-Time Correspondence as a Contrastive Random Walk
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Mast: A memory-augmented self-supervised tracker
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Self-supervised learning of pretext-invariant representations
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
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Aligning videos in space and time
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What makes for good views for contrastive learning
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