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Learning long-term spatial-temporal features are critical for many video analysis tasks.
Object segmentation by long term analysis of point trajectories
Brox, T., Malik, J.: · 2010
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Object segmentation by long term analysis of point trajectories
Brox, T., Malik, J.: · 2010
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Video segmentation by tracking many figure-ground segments
Li, F., Kim, T., Humayun, A., Tsai, D., Rehg, J.M.: · 2013
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A unified video segmentation benchmark: Annotation, metrics and analysis
Galasso, F., Nagaraja, N.S., Cárdenas, T.J., Brox, T., Schiele, B.: · 2013
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Fast object segmentation in unconstrained video
Papazoglou, A., Ferrari, V.: · 2013
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Supervoxel-consistent foreground propagation in video
Jain, S.D., Grauman, K.: · 2014
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Segmentation of moving objects by long term video analysis
Ochs, P., Malik, J., Brox, T.: · 2014
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Video segmentation by non-local consensus voting
Faktor, A., Irani, M.: · 2014
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Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: · 2015
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Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., Salakhudinov, R.: · 2015
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Learning spatiotemporal features with 3d convolutional networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: · 2015
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Jumpcut:non-successive mask transfer and interpolation for video cutout
Fan, Q., Zhong, F., Lischinski, D., Cohen-Or, D., Chen, B.: · 2015
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Activitynet: A large-scale video benchmark for human activity understanding
Heilbron, F.C., Escorcia, V., Ghanem, B., Niebles, J.C.: · 2015
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Video segmentation with just a few strokes
Nagaraja, N.S., Schmidt, F.R., Brox, T.: · 2015
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Finn, C., Goodfellow, I., Levine, S.: · 2016
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A benchmark dataset and evaluation methodology for video object segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: · 2016
Video propagation networks
Jampani, V., Gadde, R., Gehler, P.V.: · 2017
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Learning video object segmentation with visual memory
Tokmakov, P., Alahari, K., Schmid, C.: · 2017
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Maskrnn: Instance level video object segmentation
Hu, Y.T., Huang, J.B., Schwing, A.: · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: · 2017
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Learning motion patterns in videos
Tokmakov, P., Alahari, K., Schmid, C.: · 2017
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The 2017 davis challenge on video object segmentation
Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., Van Gool, L.: · 2017
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Cited alongside, same era.
Youtube-8m: A large-scale video classification benchmark
Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., Vijayanarasimhan, S.: · 2016
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End-to-end learning of driving models from large-scale video datasets
Xu, H., Gao, Y., Yu, F., Darrell, T.: · 2017
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One-shot video object segmentation
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: · 2017
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Learning video object segmentation from static images
Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., A.Sorkine-Hornung: · 2017
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Segflow: Joint learning for video object segmentation and optical flow
Cheng, J., Tsai, Y.H., Wang, S., Yang, M.H.: · 2017
Cited alongside, same era.
Fusionseg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos
Dutt Jain, S., Xiong, B., Grauman, K.: · 2017
Cited alongside, same era.
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Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., Darrell, T.: · 2017
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Online adaptation of convolutional neural networks for video object segmentation
Voigtlaender, P., Leibe, B.: · 2017
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Efficient video object segmentation via network modulation
Yang, L., Xiong, X., Wang, Y., Yang, J., Katsaggelos, A.K.: · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2018
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Youtube-vos: Sequence-to-sequence video object segmentation
Xu, N., Yang, L., Yue, D., Yang, J., Price, B., Yang, J., Cohen, S., Fan, Y., Liang, Y., Huang, T.: · 2018
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