Abstract
In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: (1) solving a submodular function for selecting object-like segments, and (2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms.
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References
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Gool, L.V.: One-shot video object segmentation. In: CVPR (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv:1606.00915 (2016)
Cheng, J., Tsai, Y.H., Wang, S., Yang, M.H.: Segflow: joint learning for video object segmentation and optical flow. In: ICCV (2017)
Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The Pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)
Faktor, A., Irani, M.: Video segmentation by non-local consensus voting. In: BMVC (2014)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: ICCV (2011)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Hoffman, J., et al.: LSDA: large scale detection through adaptation. In: NIPS (2014)
Hong, S., Oh, J., Lee, H., Han, B.: Learning transferrable knowledge for semantic segmentation with deep convolutional neural network. In: CVPR (2016)
Hu, R., Dollár, P., He, K., Darrell, T., Girshick, R.: Learning to segment every thing. arXiv:1711.10370 (2017)
Jain, S., Xiong, B., Grauman, K.: FusionSeg: learning to combine motion and appearance for fully automatic segmentation of generic objects in videos. In: CVPR (2017)
Jain, S.D., Grauman, K.: Supervoxel-consistent foreground propagation in video. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 656–671. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_43
Khoreva, A., Perazzi, F., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR (2017)
Koh, Y.J., Kim, C.S.: Primary object segmentation in videos based on region augmentation and reduction. In: CVPR (2017)
Lazic, N., Givoni, I., Frey, B., Aarabi, P.: Floss: facility location for subspace segmentation. In: ICCV (2009)
Lee, Y.J., Kim, J., Grauman, K.: Key-segments for video object segmentation. In: ICCV (2011)
Lim, J.J., Salakhutdinov, R., Torralba, A.: Transfer learning by borrowing examples for multiclass object detection. In: NIPS (2011)
Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. thesis, MIT (2009)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
Märki, N., Perazzi, F., Wang, O., Sorkine-Hornung, A.: Bilateral space video segmentation. In: CVPR (2016)
Papazoglou, A., Ferrari, V.: Fast object segmentation in unconstrained video. In: ICCV (2013)
Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: CVPR (2014)
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L.V., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR (2016)
Perazzi, F., Wang, O., Gross, M., Sorkine-Hornung, A.: Fully connected object proposals for video segmentation. In: CVPR (2015)
Ochs, P., Brox, T.: Object segmentation in video: a hierarchical variational approach for turning point trajectories into dense regions. In: ICCV (2011)
Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: CVPR (2012)
Rochan, M., Wang, Y.: Weakly supervised localization of novel objects using appearance transfer. In: CVPR (2015)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Saleh, F.S., Aliakbarian, M.S., Salzmann, M., Petersson, L., Alvarez, J.M.: Bringing background into the foreground: making all classes equal in weakly-supervised video semantic segmentation. In: ICCV (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556, 1187–1200 (2014)
Strand, R., Ciesielski, K.C., Malmberg, F., Saha, P.K.: The minimum barrier distance. CVIU 117(4), 429–437 (2013)
Tang, K., Sukthankar, R., Yagnik, J., Fei-Fei, L.: Discriminative segment annotation in weakly labeled video. In: CVPR (2013)
Taylor, B., Karasev, V., Soatto, S.: Causal video object segmentation from persistence of occlusions. In: CVPR (2015)
Tokmakov, P., Alahari, K., Schmid, C.: Learning motion patterns in videos. In: CVPR (2017)
Tokmakov, P., Alahari, K., Schmid, C.: Learning video object segmentation with visual memory. In: ICCV (2017)
Tommasi, T., Orabona, F., Caputo, B.: Learning categories from few examples with multi model knowledge transfer. PAMI 36, 928–941 (2014)
Tsai, Y.H., Yang, M.H., Black, M.J.: Video segmentation via object flow. In: CVPR (2016)
Tsai, Y.-H., Zhong, G., Yang, M.-H.: Semantic co-segmentation in videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 760–775. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_46
Yan, Y., Xu, C., Cai, D., Corso, J.J.: Weakly supervised actor-action segmentation via robust multi-task ranking. In: CVPR (2017)
Zhang, D., Yang, L., Meng, D., Xu, D., Han, J.: SPFTN: a self-paced fine-tuning network for segmenting objects in weakly labelled videos. In: CVPR (2017)
Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 fps. In: ICCV (2015)
Zhang, Y., Chen, X., Li, J., Wang, C., Xia, C.: Semantic object segmentation via detection in weakly labeled video. In: CVPR (2015)
Zhong, G., Tsai, Y.-H., Yang, M.-H.: Weakly-supervised video scene co-parsing. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10111, pp. 20–36. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54181-5_2
Zhu, F., Jiang, Z., Shao, L.: Submodular object recognition. In: CVPR (2014)
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This work is supported in part by Ministry of Science and Technology under grants MOST 105-2221-E-001-030-MY2 and MOST 107-2628-E-001-005-MY3.
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Chen, YW., Tsai, YH., Yang, CY., Lin, YY., Yang, MH. (2019). Unseen Object Segmentation in Videos via Transferable Representations. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_38
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