Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping

Baker, V.R.: Water and the martian landscape. Nature 412

2001

Earlier work this paper cites.

Baker, V.R.: Geomorphological evidence for water on mars. Elements 2

2006

Earlier work this paper cites.

Allemand, P., Delacourt, C., Gasperini, D., Kasperski, J., Pothérat, P.: Thirty years of evolution of the sedrun landslide (swisserland) from multitemporal orthorectified aerial images, differential digital terrain models and field data. Int. J. Remote Sens. Appl 1

2011

Earlier work this paper cites.

Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in neural information processing systems 28

2015

Earlier work this paper cites.

Palafox, L.F., Hamilton, C.W., Scheidt, S.P., Alvarez, A.M.: Automated detection of geological landforms on mars using convolutional neural networks. Computers & geosciences 101

2017

Earlier work this paper 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. arXiv preprint arXiv:2010.11929 (2020)

Original

2020

Earlier work this paper cites.

Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: A survey. International Journal of Computer Vision 129

2021

Earlier work this paper cites.

Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International Conference on Machine Learning. pp. 4904–4916. PMLR (2021)

2021

Earlier work this paper cites.

Julka, S., Granitzer, M., De Toffoli, B., Penasa, L., Pozzobon, R., Amerstorfer, U.: Generative adversarial networks for automatic detection of mounds in digital terrain models (mars arabia terra). In: EGU General Assembly Conference Abstracts. pp. EGU21–9188 (2021)

2021

Earlier work this paper cites.

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)

2021

Cited alongside, same era.

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning. pp. 8821–8831. PMLR (2021)

2021

Cited alongside, same era.

Jiang, S., Lian, Z., Yung, K.L., Ip, W., Gao, M.: Automated detection of multitype landforms on mars using a light-weight deep learning-based detector. IEEE Transactions on Aerospace and Electronic Systems 58

2022

Cited alongside, same era.

Julka, S.: An active learning approach for automatic detection of bow shock and magnetopause crossing signatures in mercury’s magnetosphere using messenger magnetometer observations. Proceedings of the 2nd Machine Learning in Heliophysics p. 8 (2022)

Ji, G.P., Fan, D.P., Xu, P., Cheng, M.M., Zhou, B., Van Gool, L.: Sam struggles in concealed scenes–empirical study on “ segment anything”

Original

2023

Closest in time.

Ji, W., Li, J., Bi, Q., Li, W., Cheng, L.: Segment anything is not always perfect: An investigation of sam on different real-world applications. arXiv preprint arXiv:2304.05750 (2023)

Original

2023

Closest in time.

Julka, S., Kirschstein, N., Granitzer, M., Lavrukhin, A., Amerstorfer, U.: Deep active learning for detection of mercury’s bow shock and magnetopause crossings. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part IV. pp. 452–467. Springer (2023)

2023

Closest in time.

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)

Original