Compact Image Transformer Based on Convolutional Variational Autoencoder with Augmented Attention Backbone for Target Recognition in Infrared Images

Author:

Nebili BillelORCID,Khellal Atmane,Nemra Abdelkrim,Boulahia Said Yacine,Mascarilla Laurent

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference48 articles.

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2. Devlin, J.; Chang, M.-W.; Lee, K.; Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019). https://doi.org/10.18653/v1/N19-1423

3. Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

4. Conneau, A.; Lample, G.: Cross-lingual Language Model Pretraining. In: Wallach, H.; Larochelle, H.; Beygelzimer, A.; d’ Alché-Buc, F.; Fox, E.; Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32 (2019)

5. Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)

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