[1] Fubin WANG, Hefei LIU, Jianghong HE et al. Analysis of sintering operation process parameters and construction of sintering behavior model. Sintering Pellet, 45, 29-34(2020).

[2] Fubin WANG, Hefei LIU, Rui WANG et al. Multi-core Boosting saliency detection of flame images of sintered sections. Journal of Computer Aided Design and Graphics, 33, 1466-1474(2021).

[3] Jinagyun LI, Zhifang YANG, Junfeng ZHENG et al. Application of deep learning technology in iron and steel industry. Iron and Steel, 56, 43-49(2021).

[4] A KRIZHEVSKY, I SUTSKEVER, G HINTON. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84-90.(2017).

[5] K HE, X ZHANG, S REN et al. Deep residual learning for image recognition(2016).

[6] A G HOWARD, M ZHU, B CHEN et al. MobileNets: efficient convolutional neural networks for mobile vision applications. https://arxiv.org/abs/1704.04861

[7] A DOSOVITSKIY, L BEYER, A KOLESNIKOV et al. An image is worth 16x16 words: transformers for image recognition at scale. https://www.xueshufan.com/publication/3119786062

[8] Qianchuang ZHANG, Chenxia GUO, Ruifeng YANG et al. Super resolution reconstruction of optical fiber ring image based on lightweight network. Applied Optics, 43, 913-920(2022).

[9] M MUHAMMAD, S ABDELRAHMAN, C HISHAM et al. EdgeNeXt: efficiently amalgamated cnn-transformer architecture for mobile vision applications. https://arxiv.org/abs/2206.10589v3

[10] A ZADEH, M CHEN, S PORIA et al. Tensor fusion network for multimodal sentiment analysis. https://arxiv.org/pdf/1707.07250.pdf

[11] Z LIU, Y SHEN, V B LAKSHMINARASIMHAN et al. Efficient low-rank multimodal fusion with modality-specific factors. https://arxiv.org/abs/1806.00064

[12] Z PENG, W HUANG, S GU et al. Conformer: local features coupling global representations for visual recognition. https://arxiv.org/abs/2105.03889

[13] S WOO, J PARK, J Y LEE et al. Cbam: Convolutional block attention module. https://arxiv.org/abs/1807.06521

[14] C SZEGEDY, V VANHOUCKE, S IOFFE et al. Rethinking the inception architecture for computer vision, 2818-2826(2016).

[15] M SANDLER, A HOWARD, M ZHU et al. Mobilenetv2: inverted residuals and linear bottlenecks, 4510-4520(2018).

[16] J GUO, K HAN, H WU et al. CMT: convolutional neural networks meet vision transformers. https://arxiv.org/abs/2107.06263