Abstract
There are already many mature methods for single-teacher knowledge distillation in object detection models, but the development of multi-teacher knowledge distillation methods has been slow due to the complexity of knowledge fusion among multiple teachers. In this paper, we point out that different teacher models have different detection capabilities for a certain category, and through experiments, we find that for individual target instances, there are differences in the main feature regions of the target, and the detection results also have randomness. Networks that are weak in overall detection performance for a certain category sometimes perform well, so it is not appropriate to simply select the best learning based on detection results. Therefore, we propose a novel multi-teacher distillation method that divides the central and boundary features of the instance target region through the detection of teacher models, and uses clustering to find the center of the response features of each category of teacher models as prior knowledge to guide the learning direction of the student model for the category as a whole. Since our method only needs to calculate the loss on the feature map, FGD can be applied to multiple teachers with the same components. We conducted experiments on various detectors with different backbones, and the results show that our student detector achieved excellent mAP improvement. Our distillation method achieved an mAP of 39.4% on COCO2017 based on the ResNet-50 backbone, which is higher than the single-teacher distillation learning method of the baseline model. Our code and training logs can be obtained at https://github.com/CCCCPRCV/Multi_ Neck.
This work was supported by the Chongqing Academy of Animal Sciences and the Special Project for Technological Innovation and Application Development of Chongqing Municipality (cstc2021jscx-dxwtBX0008).
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References
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points, arXiv preprint arXiv:1904.07850 (2019)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part I. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10 012–10 022 (2021)
Cai, Y., et al.: YOLOv4-5D: an effective and efficient object detector for autonomous driving. IEEE Trans. Instrum. Meas. 70, 1–13 (2021)
Dai, X., et al.: General instance distillation for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7842–7851 (2021)
Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., Xu, D.: Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst. Man Cybernet. Syst. 50(4), 1486–1498 (2018)
Chen, S.-H., Tsai, C.-C.: SMD LED chips defect detection using a YOLOv3-dense model. Adv. Eng. Inform. 47, 101255 (2021)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
Zhang, W., Li, X., Ding, Q.: Deep residual learning-based fault diagnosis method for rotating machinery. ISA Trans. 95, 295–305 (2019)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861 (2017)
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, T., Yuan, L., Zhang, X., Feng, J.: Distilling object detectors with fine-grained feature imitation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4933–4942 (2019)
Yang, Z., et al.: Focal and global knowledge distillation for detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4643–4652 (2022)
Tran, L., et al.: Hydra: preserving ensemble diversity for model distillation, arXiv preprint arXiv:2001.04694 (2020)
Liu, Y., Zhang, W., Wang, J.: Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing 415, 106–113 (2020)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Zhang, H., Chang, H., Ma, B., Wang, N., Chen, X.: Dynamic R-CNN: towards high quality object detection via dynamic training. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part XV. LNCS, vol. 12360, pp. 260–275. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58555-6_16
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement, arXiv preprint arXiv:1804.02767 (2018)
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network, arXiv preprint arXiv:1503.02531 (2015)
Mishra, A., Marr, D.: Apprentice: using knowledge distillation techniques to improve low-precision network accuracy, arXiv preprint arXiv:1711.05852 (2017)
Fukuda, T., Suzuki, M., Kurata, G., Thomas, S., Cui, J., Ramabhadran, B.: Efficient knowledge distillation from an ensemble of teachers. In: Interspeech, pp. 3697–3701 (2017)
Guo, J., et al.: Distilling object detectors via decoupled features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2154–2164 (2021)
Tan, X., Ren, Y., He, D., Qin, T., Zhao, Z., Liu, T.-Y.: Multilingual neural machine translation with knowledge distillation, arXiv preprint arXiv:1902.10461 (2019)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111, 98–136 (2015)
Chen, K., et al.: MMdetection: open MMLab detection toolbox and benchmark, arXiv preprint arXiv:1906.07155 (2019)
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Chen, J. et al. (2024). Central and Directional Multi-neck Knowledge Distillation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_38
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