[1] C Ning, W Liu, X Wang. Infrared Object Recognition Based on Monogenic Features and Multiple Kernel Learning, 204-208(2018).
[2] R M Chen, S J Liu, Z Miao et al. Infrared aircraft few-shot classification method based on meta learning. Journal of Infrared and Millimeter Waves, 40, 554-560(2021).
[3] W Li, Q Chen, G Gu et al. Visible-infrared image matching based on parameter-free attention mechanism and target-aware graph attention mechanism. Expert Systems with Applications, 238, 122038(2024).
[4] L Jin, S J Liu, X Wang et al. Infrared aircraft classification method with small samples based on improved relation network. Acta Optica Sinica, 40, 0811005(2020).
[5] X Luo, H Wu, J Zhang et al. A closer look at few-shot classification again, 202, 23103-23123(2023).
[6] X Li, X Yang, Z Ma et al. Deep metric learning for few-shot image classification: A Review of recent developments. Pattern Recognition, 138, 109381(2023).
[7] B Shi, W Li, J Huo et al. Global- and local-aware feature augmentation with semantic orthogonality for few-shot image classification. Pattern Recognition, 142, 109702(2023).
[8] R Hou, H Chang, B MA et al. Cross Attention Network for Few-shot Classification, 32(2019).
[9] D Kang, H Kwon, J Min et al. Relational Embedding for Few-Shot Classification, 8802-8813(2021).
[10] J Li. EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification, 14117, 354-365(2023).
[11] H Kwon, M Kim, S Kwak et al. Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition, 13045-13055(2021).
[12] S Lee, S Lee, H Seong et al. Revisiting Self-Similarity: Structural Embedding for Image Retrieval, 23412-23421(2023).
[13] L Wang, S Lei, J He et al. Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation, 1-10(2023).
[14] Y Zhong, Y Su, H Zhao. Self-similarity feature based few-shot learning via hierarchical relation network. International Journal of Machine Learning and Cybernetics, 14, 4237-4249(2023).
[15] L Yang, R-Y Zhang, L Li et al. SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks, 11863-11874(2021).
[16] X Wen, C Cao, Y Li et al. DRSN with Simple Parameter-Free Attention Module for Specific Emitter Identification, 192-200(2022).
[17] S Tan, L Zhang, X Shu et al. A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Frontiers of Computer Science, 17, 176338(2023).
[18] BS Webb, NT Dhruv, SG Solomon et al. Early and Late Mechanisms of Surround Suppression in Striate Cortex of Macaque. Journal of Neuroscience, 25, 11666-11675(2005).
[19] O Vinyals, C Blundell, T Lillicrap et al. Matching Networks for One Shot Learning, 29(2016).
[20] Q Liu, X Li, D Yuan et al. LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Single Object Tracking Benchmark. IEEE Transactions on Neural Networks and Learning Systems, 1-14(2023).
[21] K He, X Zhang, S Ren et al. Deep Residual Learning for Image Recognition, 770-778(2016).
[22] S Ravi, H Larochelle. Optimization as a model for few-shot learning(2017).
[23] C Finn, P Abbeel, S Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, 1126-1135(2017).
[24] R Hou, H Chang, B MA et al. Cross Attention Network for Few-shot Classification, 32(2019).
[25] K Li, Y Zhang, K Li et al. Adversarial Feature Hallucination Networks for Few-Shot Learning, 13467-13476(2020).
[26] Z Chen, J Ge, H Zhan et al. Pareto Self-Supervised Training for Few-Shot Learning, 13658-13667(2021).
[27] S Laenen, L Bertinetto. On Episodes, Prototypical Networks, and Few-Shot Learning, 34, 24581-24592(2021).
[28] Y Chen, Z Liu, H Xu et al. Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, 2021, 9062-9071.
[29] Z Qin, H Wang, CB Mawuli et al. Multi-instance attention network for few-shot learning. Information Sciences, 611, 464-475(2022).
[30] M Lazarou, T Stathaki, Y Avrithis. Tensor feature hallucination for few-shot learning, 2050-2060(2022).
[31] J Hu, L Shen, G Sun. Squeeze-and-Excitation Networks, 7132-7141(2018).
[32] S Huang, Q Wang, S Zhang et al. Dynamic Context Correspondence Network for Semantic Alignment, 2019, 2010-2019.
[33] P Ramachandran, N Parmar, A Vaswani et al. Stand-Alone Self-Attention in Vision Models, 32(2019).
[34] X Wang, R Girshick, A Gupta et al. Non-local Neural Networks, 7794-7803(2018).
[35] S Woo, J Park, J-Y Lee et al. CBAM: Convolutional Block Attention Module, 3-19(2018).