Attention Mechanism
2023
B. Psomas, I. Kakogeorgiou, K. Karantzalos, and Y. Avrithis (2023) Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?. arXiv:2309.06891, 2023-09-13. (more) (download)
A. Jamali, S. K. Roy, J. Li, and P. Ghamisi (2023) Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction. arXiv:2306.04947, 2023-06-08. (more) (download)
A. Jamali, S. K. Roy, and P. Ghamisi (2023) WetMapFormer: A Unified Deep CNN and Vision Transformer for Complex Wetland Mapping. International Journal of Applied Earth Observation and Geoinformation, 120, 103333, 2023-06-01. (more) (download)
J. Schimunek, P. Seidl, L. Friedrich, D. Kuhn, F. Rippmann, S. Hochreiter, and G. Klambauer (2023) Context-Enriched Molecule Representations Improve Few-Shot Drug Discovery. arXiv:2305.09481, 2023-04-24. (more) (download)
S. Chang & P. Ghamisi (2023) Changes to Captions: An Attentive Network for Remote Sensing Change Captioning. arXiv:2304.01091, 2023-04-03. (more) (download)
A. Jamali, S. K. Roy, A. Bhattacharya, and P. Ghamisi (2023) Local Window Attention Transformer for Polarimetric SAR Image Classification. IEEE Geoscience and Remote Sensing Letters, 2023-01-23. (more) (download)
C. H. Song, J. Yoon, S. Choi, and Y. Avrithis (2023) Boosting Vision Transformers for Image Retrieval. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 107-117, 2023-01-03. (more) (download)
2022
I. Kakogeorgiou, S. Gidaris, B. Psomas, Y. Avrithis, A. Bursuc, K. Karantzalos, and N. Komodakis (2022) What to Hide from Your Students: Attention-Guided Masked Image Modeling. arXiv:2203.12719v2, 2022-07-22. (more) (download)
M. Mc Cutchan & I. Giannopoulos (2022) Encoding Geospatial Vector Data for Deep Learning: LULC as a Use Case. Remote Sensing, 14, 12, 2812, 2022-06-11. (more) (download)
2021
F. Tang & M. Kopp (2021) A Remark on a Paper of Krotov and Hopfield. arXiv:2105.15034, 2021-06-03. (more) (download)
2020
H. Ramsauer, B. Schäfl, J. Lehner, P. Seidl, M. Widrich, L. Gruber, M. Holzleitner, M. Pavlović, G. K. Sandve, V. Greiff, D. Kreil, M. Kopp, G. Klambauer, J. Brandstetter, and S. Hochreiter (2020) Hopfield Networks is All You Need. arXiv:2008.02217, 2020-08-06. (more) (download)
M. Widrich, B. Schäfl, H. Ramsauer, M. Pavlović, L. Gruber, M. Holzleitner, J. Brandstetter, G. K. Sandve, V. Greiff, S. Hochreiter, and G. Klambauer (2020) Modern Hopfield Networks and Attention for Immune Repertoire Classification. arXiv:2007.13505, 2020-07-16. (more) (download)
2019
S. Kimeswenger, E. Rumetshofer, M. Hofmarcher, P. Tschandl, H. Kittler, S. Hochreiter, W. Hötzenecker, and G. Klambauer (2019) Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. ML4H: Machine Learning for Health workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019, or preprint at arXiv, 1911.06616v3, Image and Video Processing (eess.IV), 2019-12-02. (more) (download)