Page 294 - 《软件学报》2025年第9期
P. 294
陈建炜 等: 基于掩码信息熵迁移的场景文本检测知识蒸馏 4205
[28] Zheng ZH, Ye RG, Hou QB, Ren DW, Wang P, Zuo WM, Cheng MM. Localization distillation for object detection. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 2023, 45(8): 10070–10083. [doi: 10.1109/TPAMI.2023.3248583]
[29] Zhou SC, Liu WZ, Hu C, Zhou SC, Ma C. UniDistill: A universal cross-modality knowledge distillation framework for 3D object
detection in bird’s-eye view. In: Proc. of the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE,
2023. 5116–5125. [doi: 10.1109/CVPR52729.2023.00495]
[30] Zagoruyko S, Komodakis N. Paying more attention to attention: Improving the performance of convolutional neural networks via
attention transfer. arXiv:1612.03928, 2016.
[31] Chen JW, Yang F, Lai YX. A self-distillation approach via entropy transfer for scene text detection. Acta Automatica Sinica, 2023,
49(11): 1−12 (in Chinese with English abstract). [doi: 10.16383/j.aas.c210598]
[32] Yang P, Zhang F, Yang G. A fast scene text detector using knowledge distillation. IEEE Access, 2019, 7: 22588–22598. [doi:
10.1109/ACCESS.2019.2895330]
[33] Yang P, Yang G, Gong X, Wu PP, Han Xu, Wu JS. Instance segmentation network with self-distillation for scene text detection. IEEE
Access, 2020, 8: 45825–45836.[doi: 10.1109/ACCESS.2020.2978225]
[34] Bolya D, Zhou C, Xiao FY, Lee YJ. YOLACT: Real-time instance segmentation. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on
Computer Vision. Seoul: IEEE, 2019. 9156–9165. [doi: 10.1109/ICCV.2019.00925]
[35] Yang CL, Xie LX, Su C, Yuille AL. Snapshot distillation: Teacher-student optimization in one generation. In: Proc. of the 2019
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 2854–2863. [doi: 10.1109/CVPR.2019.00297]
[36] Zhang Y, Xiang T, Hospedales TM, Lu HC. Deep mutual learning. In: Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and
Pattern Recognition. Salt Lake City: IEEE, 2018. 4320–4328. [doi: 10.1109/CVPR.2018.00454]
[37] Ahn S, Hu SX, Damianou A, Lawrence ND, Dai ZW. Variational information distillation for knowledge transfer. In: Proc. of the 2019
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 9155–9163. [doi: 10.1109/CVPR.2019.00938]
[38] Chen HT, Wang YH, Xu C, Yang ZH, Liu CJ, Shi BX, Xu CJ, Xu C, Tian Q. Data-free learning of student networks. In: Proc. of the
2019 IEEE/CVF Int’l Conf. on Computer Vision. Seoul: IEEE, 2019. 3513–3521. [doi: 10.1109/ICCV.2019.00361]
[39] Kwon K, Na H, Lee H, Kim NS. Adaptive knowledge distillation based on entropy. In: Proc. of the 2020 IEEE Int’l Conf. on Acoustics,
Speech and Signal Processing. Barcelona: IEEE, 2020. 7409–7413. [doi: 10.1109/ICASSP40776.2020.9054698]
[40] Vu TH, Jain H, Bucher M, Cord M, Pérez P. ADVENT: Adversarial entropy minimization for domain adaptation in semantic
segmentation. In: Proc. of the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 2512–2521.
[doi: 10.1109/CVPR.2019.00262]
[41] Lin TY, Dollár P, Girshick R, He KM, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proc. of the 2017
IEEE Conf. on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 936–944. [doi: 10.1109/CVPR.2017.106]
[42] Karatzas D, Shafait F, Uchida S, Iwamura M, Bigorda LGI, Mestre SR, Mas J, Mota DF, Almazàn JA, de las Heras LP. ICDAR 2013
robust reading competition. In: Proc. of the 12th Int’l Conf. on Document Analysis and Recognition. Washington: IEEE, 2013.
1484–1493. [doi: 10.1109/ICDAR.2013.221]
[43] Yao C, Bai X, Liu WY, Ma Y, Tu ZW. Detecting texts of arbitrary orientations in natural images. In: Proc. of the 2012 IEEE Conf. on
Computer Vision and Pattern Recognition. Providence: IEEE, 2012. 1083–1090. [doi: 10.1109/CVPR.2012.6247787]
[44] Yao C, Bai X, Liu WY. A unified framework for multioriented text detection and recognition. IEEE Trans. on Image Processing, 2014,
23(11): 4737–4749. [doi: 10.1109/TIP.2014.2353813]
[45] Ch’ng CK, Chan CS. Total-Text: A comprehensive dataset for scene text detection and recognition. In: Proc. of the 14th IAPR Int’l Conf.
on Document Analysis and Recognition. Kyoto: IEEE, 2017. 935–942. [doi: 10.1109/ICDAR.2017.157]
[46] He WH, Zhang XY, Yin F, Liu CL. Multi-oriented and multi-lingual scene text detection with direct regression. IEEE Trans. on Image
Processing, 2018, 27(11): 5406–5419. [doi: 10.1109/TIP.2018.2855399]
[47] Howard A, Sandler M, Chen B, Wang WJ, Chen LC, Tan MX, Chu G, Vasudevan V, Zhu YK, Pang RM, Adam H, Le Q. Searching for
MobileNetV3. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on Computer Vision. Seoul: IEEE, 2019. 1314–1324. [doi: 10.1109/ICCV.
2019.00140]
[48] Shu CY, Liu YF, Gao JF, Yan Z, Shen CH. Channel-wise knowledge distillation for dense prediction. In: Proc. of the 2021 IEEE/CVF Int’l
Conf. on Computer Vision. Montreal: IEEE, 2021. 5291–5300. [doi: 10.1109/ICCV48922.2021.00526]
[49] Wang YK, Zhou W, Jiang T, Bai X, Xu YC. Intra-class feature variation distillation for semantic segmentation. In: Proc. of the 16th
European Conf. on Computer Vision. Glasgow: Springer, 2020. 346–362. [doi: 10.1007/978-3-030-58571-6_21]
[50] Cho JH, Hariharan B. On the efficacy of knowledge distillation. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on Computer Vision. Seoul:
IEEE, 2019. 4793–4801. [doi: 10.1109/ICCV.2019.00489]

