SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders

Fischler, M.A., Elschlager, R.A.: The representation and matching of pictorial structures. TC 100

1973

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: CVPR. pp. 248–255 (2009)

2009

Earlier work this paper cites.

Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32

2009

Earlier work this paper cites.

Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)

2011

Earlier work this paper cites.

Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops. pp. 554–561 (2013)

2013

Earlier work this paper cites.

Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.L.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: CVPR. pp. 1979–1986 (2014)

2014

Earlier work this paper cites.

Krause, J., Jin, H., Yang, J., Fei-Fei, L.: Fine-grained recognition without part annotations. In: CVPR. pp. 5546–5555 (2015)

2015

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

Original

2016

Earlier work this paper cites.

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: CVPR. pp. 2536–2544 (2016)

2016

Earlier work this paper cites.

Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)

Original

2017

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

Original

2017

Earlier work this paper cites.

Van Den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. NeurIPS 30

2017

Earlier work this paper cites.

Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: CVPR. pp. 5209–5217 (2017)

2017

Earlier work this paper cites.

Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: CVPR. pp. 633–641 (2017)

2017

Earlier work this paper cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Cited alongside, same era.

Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8769–8778 (2018)

2018

Cited alongside, same era.

Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: ECCV. pp. 418–434 (2018)

2018

Cited alongside, same era.

Zheng, H., Fu, J., Zha, Z.J., Luo, J.: Learning deep bilinear transformation for fine-grained image representation. NeurIPS 32

2019

Cited alongside, same era.

He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021)

Original

2021

Later among the works it cites.

Hinton, G.: How to represent part-whole hierarchies in a neural network. arXiv preprint arXiv:2102.12627 (2021)

Original

2021

Later among the works it cites.

Li, Z., Chen, Z., Yang, F., Li, W., Zhu, Y., Zhao, C., Deng, R., Wu, L., Zhao, R., Tang, M., et al.: Mst: Masked self-supervised transformer for visual representation. NeurIPS 34

2021

Later among the works it cites.

Saha, O., Cheng, Z., Maji, S.: Ganorcon: Are generative models useful for few-shot segmentation? arXiv preprint arXiv:2112.00854 (2021)

Original

2021

Later among the works it cites.