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Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results.
Principal warps: Thin-plate splines and the decomposition of deformations
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Region filling and object removal by exemplar-based image inpainting
A. Criminisi, P. Perez, and K. Toyama · 2004
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Grabcut: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
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Poisson matting
J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum · 2004
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Robust tracking-by-detection using a detector confidence particle filter
M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. Van Gool · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Efficient hierarchical graph-based video segmentation
M. Grundmann, V. Kwatra, M. Han, and I. Essa · 2010
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Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
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Exploiting the circulant structure of tracking-by-detection with kernels
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista · 2012
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Articulated people detection and pose estimation: Reshaping the future
L. Pishchulin, A. Jain, M. Andriluka, T. Thormählen, and B. Schiele · 2012
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Learning object class detectors from weakly annotated video
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari · 2012
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Shifting weights: Adapting object detectors from image to video
K. Tang, V. Ramanathan, L. Fei-fei, and D. Koller · 2012
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A video representation using temporal superpixels
J. Chang, D. Wei, and J. W. Fisher · 2013
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Video segmentation by tracking many figure-ground segments
F. Li, T. Kim, A. Humayun, D. Tsai, and J. M. Rehg · 2013
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Fast object segmentation in unconstrained video
A. Papazoglou and V. Ferrari · 2013
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Learning people detectors for tracking in crowded scenes
S. Tang, M. Andriluka, A. Milan, K. Schindler, S. Roth, and B. Schiele · 2013
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Video segmentation by non-local consensus voting
A. Faktor and M. Irani · 2014
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Supervoxel-consistent foreground propagation in video
S. D. Jain and K. Grauman · 2014
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The visual object tracking vot2014 challenge results
M. Kristan, R. Pflugfelder, et al · 2014
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Touchcut: Fast image and video segmentation using single-touch interaction
T. Wang, B. Han, and J. Collomosse · 2014
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Visual saliency by extended quantum cuts
Ç. Aytekin, E. C. Ozan, S. Kiranyaz, and M. Gabbouj · 2015
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Convolutional features for correlation filter based visual tracking
M. Danelljan, G. Hager, F. Shahbaz Khan, and M. Felsberg · 2015
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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The visual object tracking vot2015 challenge results
M. Kristan, J. Matas, et al · 2015
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Hierarchical convolutional features for visual tracking
C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang · 2015
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Video segmentation with just a few strokes
N. Nagaraja, F. Schmidt, and T. Brox · 2015
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Articulated pose estimation with tiny synthetic videos
D. Park and D. Ramanan · 2015
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Fully connected object proposals for video segmentation
F. Perazzi, O. Wang, M. Gross, and A. Sorkine-Hornung · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Visual tracking with fully convolutional networks
L. Wang, W. Ouyang, X. Wang, and H. Lu · 2015
Cited alongside, same era.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Fully-convolutional siamese networks for object tracking
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr · 2016
Track and segment: An iterative unsupervised approach for video object proposals
F. Xiao and Y. J. Lee · 2016
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Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
J. J. Yu, A. W. Harley, and K. G. Derpanis · 2016
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Pixelnet: Representation of the pixels, by the pixels, and for the pixels
A. Bansal, X. Chen, B. Russell, A. Gupta, and D. Ramanan · 2017
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One-shot video object segmentation
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixe, D. Cremers, and L. V. Gool · 2017
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Rethinking atrous convolution for semantic image segmentation
L. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Cited alongside, same era.
Synthesizing training images for boosting human 3d pose estimation
W. Chen, H. Wang, Y. Li, H. Su, Z. Wang, C. Tu, D. Lischinski, D. Cohen-Or, and B. Chen · 2016
Cited alongside, same era.
Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
Cited alongside, same era.
Learning to track at 100 fps with deep regression networks
D. Held, S. Thrun, and S. Savarese · 2016
Cited alongside, same era.
Click carving: Segmenting objects in video with point clicks
S. D. Jain and K. Grauman · 2016
Cited alongside, same era.
V. Jampani, R. Gadde, and P. V. Gehler · 2016
Cited alongside, same era.
Learning to segment instances in videos with spatial propagation network
J. Cheng, S. Liu, Y.-H. Tsai, W.-C. Hung, S. Gupta, J. Gu, J. Kautz, S. Wang, and M.-H. Yang · 2017
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Synthesizing training data for object detection in indoor scenes
G. Georgakis, A. Mousavian, A. C. Berg, and J. Kosecka · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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S. D. Jain, B. Xiong, and K. Grauman · 2017
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Lucid data dreaming for object tracking
A. Khoreva, R. Benenson, E. Ilg, T. Brox, and B. Schiele · 2017
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Instance re-identification flow for video object segmentation
T. N. Le, K. T. Nguyen, M. H. Nguyen-Phan, T. V. Ton, T. A. Nguyen, X. S. Trinh, Q. H. Dinh, V. T. Nguyen, A. D. Duong, A. Sugimoto, T. V. Nguyen, and M. T. Tran · 2017
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Video object segmentation with re-identification
X. Li, Y. Qi, Z. Wang, K. Chen, Z. Liu, J. Shi, P. Luo, C. C. Loy, and X. Tang · 2017
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Full-resolution residual networks for semantic segmentation in street scenes
T. Pohlen, A. Hermans, M. Mathias, and B. Leibe · 2017
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Davis challenge on video object segmentation 2017
J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool · 2017
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The 2017 davis challenge on video object segmentation
J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool · 2017
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Multiple-instance video segmentation with sequence-specific object proposals
A. Shaban, A. Firl, A. Humayun, J. Yuan, X. Wang, P. Lei, N. Dhanda, B. Boots, J. M. Rehg, and F. Li · 2017
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Video object segmentation using tracked object proposals
G. Sharir, E. Smolyansky, and I. Friedman · 2017
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Learning video object segmentation with visual memory
P. Tokmakov, K. Alahari, and C. Schmid · 2017
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Online adaptation of convolutional neural networks for the 2017 davis challenge on video object segmentation
P. Voigtlaender and B. Leibe · 2017
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Online adaptation of convolutional neural networks for video object segmentation
P. Voigtlaender and B. Leibe · 2017
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Pixel-wise object segmentations for the VOT 2016 dataset
T. Vojir and J. Matas · 2017
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Super-trajectory for video segmentation
W. Wang and J. Shen · 2017
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Some promising ideas about multi-instance video segmentation
H. Zhao · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Y. Zhu, Z. Lan, S. Newsam, and A. G. Hauptmann · 2017
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Deep spatio-temporal random fields for efficient video segmentation
S. Chandra, C. Couprie, and I. Kokkinos · 2018
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PReMVOS: Proposal-generation, refinement and merging for video object segmentation
B. L. J. Luiten, P. Voigtlaender · 2018
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Pyramid dilated deeper convlstm for video salient object detection
H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam · 2018
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