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We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation.
Robust object tracking by hierarchical association of detection responses
C. Huang, B. Wu, and R. Nevatia · 2008
Earlier work this paper cites.
Visual object tracking using adaptive correlation filters
D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui · 2010
Earlier work this paper cites.
Online object tracking: A benchmark
Y. Wu, J. Lim, and M.-H. Yang · 2013
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
High-speed tracking with kernelized correlation filters
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista · 2015
Earlier work this paper cites.
Hierarchical convolutional features for visual tracking
C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Object tracking benchmark
Y. Wu, J. Lim, and M.-H. Yang · 2015
Earlier work this paper cites.
Beyond correlation filters: Learning continuous convolution operators for visual tracking
M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
A novel performance evaluation methodology for single-target trackers
M. Kristan, J. Matas, A. Leonardis, T. Vojir, R. Pflugfelder, G. Fernandez, G. Nebehay, F. Porikli, and L. Čehovin · 2016
Earlier work this paper cites.
A benchmark and simulator for uav tracking
M. Mueller, N. Smith, and B. Ghanem · 2016
Earlier work this paper cites.
Learning multi-domain convolutional neural networks for visual tracking
H. Nam and B. Han · 2016
Earlier work this paper cites.
A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
Earlier work this paper cites.
Tensorpack
Y. Wu et al · 2016
Earlier work this paper cites.
One-shot video object segmentation
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool · 2017
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
Earlier work this paper cites.
ECO: Efficient convolution operators for tracking
M. Danelljan, G. Bhat, F. S. Khan, and M. Felsberg · 2017
Earlier work this paper cites.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
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The Mapillary Vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. R. Bulo, and P. Kontschieder · 2017
Cited alongside, same era.
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
Cited alongside, same era.
YouTube-BoundingBoxes: A large high-precision human-annotated data set for object detection in video
E. Real, J. Shlens, S. Mazzocchi, X. Pan, and V. Vanhoucke · 2017
Cited alongside, same era.
CREST: Convolutional residual learning for visual tracking
Y. Song, C. Ma, L. Gong, J. Zhang, R. Lau, and M.-H. Yang · 2017
Cited alongside, same era.
PReMVOS: Proposal-generation, refinement and merging for video object segmentation
J. Luiten, P. Voigtlaender, and B. Leibe · 2018
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Video object segmentation without temporal information
K.-K. Maninis, S. Caelles, Y. Chen, J. Pont-Tuset, L. L. Taixé, and L. Van Gool · 2018
Later among the works it cites.
TrackingNet: A large-scale dataset and benchmark for object tracking in the wild
M. Müller, A. Bibi, S. Giancola, S. Al-Subaihi, and B. Ghanem · 2018
Later among the works it cites.
VITAL: Visual tracking via adversarial learning
Y. Song, C. Ma, X. Wu, L. Gong, L. Bao, W. Zuo, C. Shen, R. Lau, and M.-H. Yang · 2018
Later among the works it cites.
Learning spatial-aware regressions for visual tracking
C. Sun, D. Wang, H. Lu, and M. Yang · 2018
Later among the works it cites.
Long-term tracking in the wild: A benchmark
J. Valmadre, L. Bertinetto, J. F. Henriques, R. Tao, A. Vedaldi, A. W. M. Smeulders, P. H. S. Torr, and E. Gavves · 2018
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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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CNN in MRF: video object segmentation via inference in a cnn-based higher-order spatio-temporal MRF
L. Bao, B. Wu, and W. Liu · 2018
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Cascade r-cnn: Delving into high quality object detection
Z. Cai and N. Vasconcelos · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Blazingly fast video object segmentation with pixel-wise metric learning
Y. Chen, J. Pont-Tuset, A. Montes, and L. Van Gool · 2018
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Fast and accurate online video object segmentation via tracking parts
J. Cheng, Y.-H. Tsai, W.-C. Hung, S. Wang, and M.-H. Yang · 2018
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Group normalization
Y. Wu and K. He · 2018
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Fast video object segmentation by reference-guided mask propagation
S. Wug Oh, J.-Y. Lee, K. Sunkavalli, and S. Joo Kim · 2018
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YouTube-VOS: Sequence-to-sequence video object segmentation
N. Xu, L. Yang, Y. Fan, J. Yang, D. Yue, Y. Liang, B. Price, S. Cohen, and T. Huang · 2018
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Efficient video object segmentation via network modulation
L. Yang, Y. Wang, X. Xiong, J. Yang, and A. K. Katsaggelos · 2018
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Vision meets drones: A challenge
P. Zhu, L. Wen, X. Bian, L. Haibin, and Q. Hu · 2018
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Distractor-aware siamese networks for visual object tracking
Z. Zhu, Q. Wang, B. Li, W. Wu, J. Yan, and W. Hu · 2018
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LaSOT: A high-quality benchmark for large-scale single object tracking
H. Fan, L. Lin, F. Yang, P. Chu, G. Deng, S. Yu, H. Bai, Y. Xu, C. Liao, and H. Ling · 2019
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Siamese cascaded region proposal networks for real-time visual tracking
H. Fan and H. Ling · 2019
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SiamRPN++: Evolution of siamese visual tracking with very deep networks
B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan · 2019
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4d generic video object proposals
A. Ošep, P. Voigtlaender, M. Weber, J. Luiten, and B. Leibe · 2019
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Feelvos: Fast end-to-end embedding learning for video object segmentation
P. Voigtlaender, Y. Chai, F. Schroff, H. Adam, B. Leibe, and L.-C. Chen · 2019
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Siam R-CNN: Visual tracking by re-detection
P. Voigtlaender, J. Luiten, P. H. S. Torr, and B. Leibe · 2019
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Fast online object tracking and segmentation: A unifying approach
Q. Wang, L. Zhang, L. Bertinetto, W. Hu, and P. H. Torr · 2019
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Deeper and wider siamese networks for real-time visual tracking
Z. Zhang, H. Peng, and Q. Wang · 2019
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