[1] Xu C J, Zheng X T, Lu X Q. Multi-level alignment network for cross-domain ship detection[J]. Remote Sensing, 14, 2389(2022).

[2] Zheng X T, Zhang Y C, Lu X Q. Deep-balanced discrete hashing for image retrieval[J]. Neurocomputing, 403, 224-236(2020).

[3] Zheng X T, Wang B Q, Du X Q et al. Mutual attention inception network for remote-sensing visual question answering[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[4] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

[5] Wu F, Yang X, Ma Y et al. Machine-learning-guided optimization of laser pulses for direct-drive implosions[J]. High Power Laser Science and Engineering, 10, e12(2022).

[6] Wang X, Duan L, Shi A Y et al. Multilevel feature fusion networks with adaptive channel dimensionality reduction for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 8010205(2022).

[7] Ji J S, Zhang T, Jiang L F et al. Combining multilevel features for remote sensing image scene classification with an attention model[J]. IEEE Geoscience and Remote Sensing Letters, 17, 1647-1651(2020).

[8] Chen X M, Zheng X T, Zhang Y et al. Remote sensing scene classification by local-global mutual learning[J]. IEEE Geoscience and Remote Sensing Letters, 19, 6506405(2022).

[9] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

[10] Chen C F R, Fan Q F, Panda R. CrossViT: cross-attention multi-scale vision transformer for image classification[C], 347-356(2022).

[11] Strudel R, Garcia R, Laptev I et al. Segmenter: transformer for semantic segmentation[C], 7242-7252(2022).

[12] Liu Z, Lin Y T, Cao Y et al. Swin Transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2022).

[13] Song H, Sun D Q, Chun S et al. ViDT: an efficient and effective fully transformer-based object detector[EB/OL]. https://arxiv.org/abs/2110.03921

[14] Cui Y T, Jiang C, Wang L M et al. MixFormer: end-to-end tracking with iterative mixed attention[C], 13598-13608(2022).

[15] Bazi Y, Bashmal L, Rahhal M M et al. Vision transformers for remote-sensing image classification[J]. Remote Sensing, 13, 516(2021).

[16] Zhang J R, Zhao H W, Li J. TRS: transformers for remote sensing scene classification[J]. Remote Sensing, 13, 4143(2021).

[17] Song H, Sun D Q, Chun S et al. ViDT: an efficient and effective fully transformer-based object detector[EB/OL]. https://arxiv.org/abs/2110.03921

[18] Abnar S, Zuidema W. Quantifying attention flow in transformers[EB/OL]. https://arxiv.org/abs/2005.00928

[19] Sun H, Li S Y, Zheng X T et al. Remote sensing scene classification by a gated bidirectional network[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 82-96(2020).

[20] Li E Z, Samat A, Du P J et al. Improved bilinear CNN model for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 8004305(2022).

[21] Cao R, Fang L Y, Lu T et al. Self-attention-based deep feature fusion for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 18, 43-47(2021).

[22] Zhao Z C, Li J Q, Luo Z et al. Remote sensing image scene classification was based on an enhanced attention module[J]. IEEE Geoscience and Remote Sensing Letters, 18, 1926-1930(2021).

[23] Liang J L, Deng Y F, Zeng D. A deep neural network combined CNN and GCN for remote-sensing scene classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4325-4338(2020).

[24] Wang Q, Huang W, Xiong Z T et al. Looking closer at the scene: multiscale representation learning for remote-sensing image scene classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 33, 1414-1428(2022).