Fetching the paper…
Reading the bibliography…
This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios.
Polyak, B.T., Juditsky, A.B.: Acceleration of stochastic approximation by averaging. SIAM journal on control and optimization 30
1992
Earlier work this paper cites.
Badrinarayanan, V., Galasso, F., Cipolla, R.: Label propagation in video sequences. In: CVPR. pp. 3265–3272. IEEE (2010)
2010
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88
2010
Earlier work this paper cites.
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV. pp. 991–998. IEEE (2011)
2011
Earlier work this paper cites.
Ngan, K.N., Li, H.: Video segmentation and its applications. Springer Science & Business Media (2011)
2011
Earlier work this paper cites.
Vijayanarasimhan, S., Grauman, K.: Active frame selection for label propagation in videos. In: ECCV. pp. 496–509. Springer (2012)
2012
Earlier work this paper cites.
Avinash Ramakanth, S., Venkatesh Babu, R.: Seamseg: Video object segmentation using patch seams. In: CVPR. pp. 376–383 (2014)
2014
Earlier work this paper cites.
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. TPAMI 37
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Nowozin, S.: Optimal decisions from probabilistic models: the intersection-over-union case. In: CVPR. pp. 548–555 (2014)
2014
Earlier work this paper cites.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
2015
Earlier work this paper cites.
Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended cssd. TPAMI 38
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. In: NIPS Workshops (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: ECCV. pp. 646–661. Springer (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR. pp. 724–732 (2016)
2016
Earlier work this paper cites.
Zhang, Z., Fidler, S., Urtasun, R.: Instance-level segmentation for autonomous driving with deep densely connected mrfs. In: CVPR. pp. 669–677 (2016)
2016
Earlier work this paper cites.
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixé, L., Cremers, D., Van Gool, L.: One-shot video object segmentation. In: CVPR. pp. 221–230 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR. pp. 2117–2125 (2017)
2017
Earlier work this paper cites.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
Earlier work this paper cites.
Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR. pp. 2663–2672 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NIPS (2017)
2017
Cited alongside, same era.
Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. In: BMVC (2017)
2017
Cited alongside, same era.
Chen, Y., Pont-Tuset, J., Montes, A., Van Gool, L.: Blazingly fast video object segmentation with pixel-wise metric learning. In: CVPR. pp. 1189–1198 (2018)
Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: Feelvos: Fast end-to-end embedding learning for video object segmentation. In: CVPR. pp. 9481–9490 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Yang, L., Fan, Y., Xu, N.: Video instance segmentation. In: ICCV. pp. 5188–5197 (2019)
2019
Later among the works it cites.
Bertasius, G., Torresani, L.: Classifying, segmenting, and tracking object instances in video with mask propagation. In: CVPR. pp. 9739–9748 (2020)
2020
Later among the works it cites.
Bhat, G., Lawin, F.J., Danelljan, M., Robinson, A., Felsberg, M., Van Gool, L., Timofte, R.: Learning what to learn for video object segmentation. In: ECCV (2020)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Hu, Y.T., Huang, J.B., Schwing, A.G.: Videomatch: Matching based video object segmentation. In: ECCV. pp. 54–70 (2018)
2018
Cited alongside, same era.
Luiten, J., Voigtlaender, P., Leibe, B.: Premvos: Proposal-generation, refinement and merging for video object segmentation. In: ACCV. pp. 565–580 (2018)
2018
Cited alongside, same era.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. In: ICCV. pp. 4055–4064. PMLR (2018)
2018
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR. pp. 4510–4520 (2018)
2018
Cited alongside, same era.
Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. In: NAACL. pp. 464–468 (2018)
2018
Cited alongside, same era.
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR. pp. 7794–7803 (2018)
2018
Cited alongside, same era.
Wu, Y., He, K.: Group normalization. In: ECCV. pp. 3–19 (2018)
2018
Cited alongside, same era.
2020
Later among the works it cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV. pp. 213–229. Springer (2020)
2020
Later among the works it cites.
Chen, X., Li, Z., Yuan, Y., Yu, G., Shen, J., Qi, D.: State-aware tracker for real-time video object segmentation. In: CVPR. pp. 9384–9393 (2020)
2020
Later among the works it cites.
Li, Y., Shen, Z., Shan, Y.: Fast video object segmentation using the global context module. In: ECCV. pp. 735–750. Springer (2020)
2020
Later among the works it cites.
Lu, X., Wang, W., Danelljan, M., Zhou, T., Shen, J., Van Gool, L.: Video object segmentation with episodic graph memory networks. In: ECCV (2020)
2020
Later among the works it cites.
Miao, J., Wei, Y., Yang, Y.: Memory aggregation networks for efficient interactive video object segmentation. In: CVPR (2020)
2020
Later among the works it cites.
Seong, H., Hyun, J., Kim, E.: Kernelized memory network for video object segmentation. In: ECCV (2020)
2020
Later among the works it cites.
Synnaeve, G., Xu, Q., Kahn, J., Likhomanenko, T., Grave, E., Pratap, V., Sriram, A., Liptchinsky, V., Collobert, R.: End-to-end asr: from supervised to semi-supervised learning with modern architectures. In: ICML Workshops (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Wang, Z., Zheng, L., Liu, Y., Wang, S.: Towards real-time multi-object tracking. In: ECCV. Springer (2020)
2020
Later among the works it cites.
Xu, Z., Zhang, W., Tan, X., Yang, W., Huang, H., Wen, S., Ding, E., Huang, L.: Segment as points for efficient online multi-object tracking and segmentation. In: ECCV. pp. 264–281. Springer (2020)
2020
Later among the works it cites.
Yang, Z., Wei, Y., Yang, Y.: Collaborative video object segmentation by foreground-background integration. In: ECCV (2020)
2020
Later among the works it cites.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
2021
Closest in time.
Duke, B., Ahmed, A., Wolf, C., Aarabi, P., Taylor, G.W.: Sstvos: Sparse spatiotemporal transformers for video object segmentation. In: CVPR (2021)
2021
Closest in time.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (2021)
2021
Closest in time.
Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., Shlens, J.: Scaling local self-attention for parameter efficient visual backbones. In: CVPR. pp. 12894–12904 (2021)
2021
Closest in time.
Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., Xia, H.: End-to-end video instance segmentation with transformers. In: CVPR. pp. 8741–8750 (2021)
2021
Closest in time.
Yang, Z., Wei, Y., Yang, Y.: Collaborative video object segmentation by multi-scale foreground-background integration. TPAMI (2021)
2021
Closest in time.