[2] J L Z Ma, Y Qi, Z Wang. User Intension Understanding from Scratch, Processing and Learning for Intelligent Machines (SPLINE), 1(2016).

[4] G Li, Z Li, C Zhang et al. Shellfish Detection Based on Fusion Attention Mechanism in End-to-End Network, Proceedings of Pattern Recognition and Computer Vision(PRCV), 516(2019).

[5] A Mallya, P Molchanov, S Tyree et al. Importance Estimation for Neural Network Pruning, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11264(2019).

[6] V Palakkode, X Suau, L Zappella et al. Filter Distillation for Network Compression, 2018, arXiv:, 08955(2004).

[7] N Ma, X Zhang, H T Zheng et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, European Conference on Computer Vision(ECCV), 116(2018).

[8] A Achkar, P-M Jodoin, C Lemaire. Structured Pruning of Neural Networks With Budget-Aware Regularization, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9108(2019).

[9] N Xu, L Yang, J Yu et al. Slimmable Neural Networks, 7th International Conference on Learning Representations, ICLR(2019).

[10] T S Huang, J Yu. Universally Slimmable Networks and Improved Training Techniques, Proceedings of the IEEE/CVF International Conference on Computer Vision, 1803(2019).

[11] H Cai, C Gan, T Wang et al. Once-For-All: Train One Network and Specialize It for Efficient Deployment, International Conference on Learning Representations (ICLR), 2020.

[12] S Yan, T Yang, S Zhu et al. MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution, European Conference on Computer Vision(ECCV), 299(2020).

[13] C Wu, H Zhang, Z Zhang et al. ResNest: Split-Attention Networks, 2020, arXiv:, 08955(2004).