Learning What to Learn for Video Object Segmentation

Cohen, I., Medioni, G.: Detecting and tracking moving objects for video surveillance. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149). vol. 2, pp. 319–325. IEEE (1999)

1999

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR09 (2009)

2009

Earlier work this paper cites.

Erdélyi, A., Barát, T., Valet, P., Winkler, T., Rinner, B.: Adaptive cartooning for privacy protection in camera networks. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 44–49. IEEE (2014)

2014

Earlier work this paper cites.

Kingma, D., Ba, J.: Adam: A method for stochastic optimization. International Conference on Learning Representations (12 2014)

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: European conference on computer vision. pp. 740–755. Springer (2014)

2014

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: ICCV (2015)

2015

Earlier work this paper cites.

Ros, G., Ramos, S., Granados, M., Bakhtiary, A., Vazquez, D., Lopez, A.M.: Vision-based offline-online perception paradigm for autonomous driving. In: 2015 IEEE Winter Conference on Applications of Computer Vision. pp. 231–238. IEEE (2015)

2015

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: Computer Vision and Pattern Recognition (2016)

2016

Earlier work this paper cites.

Saleh, K., Hossny, M., Nahavandi, S.: Kangaroo vehicle collision detection using deep semantic segmentation convolutional neural network. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). pp. 1–7. IEEE

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: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5320–5329. IEEE (2017)

2017

Earlier work this paper cites.

Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1126–1135. JMLR. org (2017)

2017

Earlier work this paper cites.

He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask r-cnn. 2017 IEEE International Conference on Computer Vision (ICCV) pp. 2980–2988 (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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2663–2672 (2017)

2017

Earlier work this paper cites.

Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for video object segmentation. In: BMVC (2017)

2017

Cited alongside, same era.

Behl, H.S., Najafi, M., Arnab, A., Torr, P.H.S.: Meta learning deep visual words for fast video object segmentation. In: NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving (2018)

2018

Cited alongside, same era.

Berman, M., Rannen Triki, A., Blaschko, M.B.: The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4413–4421 (2018)

2018

Cited alongside, same era.

Hu, Y.T., Huang, J.B., Schwing, A.G.: Videomatch: Matching based video object segmentation. In: European Conference on Computer Vision. pp. 56–73. Springer (2018)

2018

Cited alongside, same era.

Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 6182–6191 (2019)

2019

Later among the works it cites.

Choi, J., Kwon, J., Lee, K.M.: Deep meta learning for real-time target-aware visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 911–920 (2019)

2019

Later among the works it cites.

Johnander, J., Danelljan, M., Brissman, E., Khan, F.S., Felsberg, M.: A generative appearance model for end-to-end video object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

2019

Later among the works it cites.

Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: CVPR (2019)

2019

Later among the works it cites.