參考文獻 |
[1] A. Krizhevsky, I. Sutskever, & G.E. Hinton, (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84 - 90.
[2] K. Simonyan, & A. Zisserman, (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
[3] K. He, X. Zhang, S. Ren, & J. Sun, (2015). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
[4] R.B. Girshick, J. Donahue, T. Darrell, & J. Malik, (2013). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
[5] R.B. Girshick, (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), 1440-1448.
[6] S. Ren, K. He, R.B. Girshick, & J. Sun, (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149.
[7] J. Redmon, S.K. Divvala, R.B. Girshick, & A. Farhadi, (2015). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788.
[8] J. Redmon, & A. Farhadi, (2016). YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517-6525.
[9] J. Redmon, & A. Farhadi, (2018). YOLOv3: An Incremental Improvement. ArXiv, abs/1804.02767.
[10] A. Bochkovskiy, C. Wang, & H.M. Liao, (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv, abs/2004.10934.
[11] G. Jocher, (2022). YOLOv5 release v6.1 https://github.com/ ultralytics/yolov5/releases/tag/v6.1. 2022
[12] C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, L. Li, Z. Ke, Q. Li, M. Cheng, W. Nie, Y. Li, B. Zhang, Y. Liang, L. Zhou, X. Xu, X. Chu, X. Wei, & X. Wei, (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. ArXiv, abs/2209.02976.
[13] C. Wang, A. Bochkovskiy, & H.M. Liao, (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. ArXiv, abs/2207.02696.
[14] E. Shelhamer, J. Long, & T. Darrell, (2014). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431-3440.
[15] O. Ronneberger, P. Fischer, & T. Brox, (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv, abs/1505.04597.
[16] A. Vaswani, N.M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, & I. Polosukhin, (2017). Attention is All you Need. ArXiv, abs/1706.03762.
[17] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, & S. Zagoruyko, (2020). End-to-End Object Detection with Transformers. ArXiv, abs/2005.12872.
[18] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, & N. Houlsby, (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ArXiv, abs/2010.11929.
[19] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, & B. Guo, (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 9992-10002.
[20] W. Wang, E. Xie, X. Li, D. Fan, K. Song, D. Liang, T. Lu, P. Luo, & L. Shao, (2021). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 548-558.
[21] M. Defferrard, X. Bresson, & P. Vandergheynst, (2016). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS.
[22] T. Kipf, & M. Welling, (2016). Semi-Supervised Classification with Graph Convolutional Networks. ArXiv, abs/1609.02907.
[23] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio’, & Y. Bengio, (2017). Graph Attention Networks. ArXiv, abs/1710.10903.
[24] K. Han, Y. Wang, J. Guo, Y. Tang, & E. Wu, (2022). Vision GNN: An Image is Worth Graph of Nodes. ArXiv, abs/2206.00272.
[25] G. Zhao, W. Ge, & Y. Yu, (2021). GraphFPN: Graph Feature Pyramid Network for Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2743-2752.
[26] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, & Y. Wei, (2017). Deformable Convolutional Networks. 2017 IEEE International Conference on Computer Vision 37(ICCV), 764-773.
[27] T. Lin, P. Dollár, R.B. Girshick, K. He, B. Hariharan, & S.J. Belongie, (2016). Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936-944.
[28] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S.E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, & A. Rabinovich, (2014). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
[29] X. Zhu, H. Hu, S. Lin, & J. Dai, (2018). Deformable ConvNets V2: More Deformable, Better Results. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9300-9308.
[30] W. Wang, J. Dai, Z. Chen, Z. Huang, Z. Li, X. Zhu, X. Hu, T. Lu, L. Lu, H. Li, X. Wang, & Y. Qiao, (2022). InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions. ArXiv, abs/2211.05778.
[31] X. Zhu, W. Su, L. Lu, B. Li, X. Wang, & J. Dai, (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection. ArXiv, abs/2010.04159.
[32] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, & P.S. Yu, (2019). A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 32, 4-24.
[33] J. Gilmer, S.S. Schoenholz, P.F. Riley, O. Vinyals, & G.E. Dahl, (2017). Neural Message Passing for Quantum Chemistry. ArXiv, abs/1704.01212.159.
[34] T. Lin, M. Maire, S.J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, & C.L. Zitnick, (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision.
[35] L.N. Smith, & N. Topin, (2017). Super-convergence: very fast training of neural networks using large learning rates. Defense + Commercial Sensing.
[36] Z. Ge, S. Liu, F. Wang, Z. Li, & J. Sun, (2021). YOLOX: Exceeding YOLO Series in 2021. ArXiv, abs/2107.08430.
[37] S. Xu, X. Wang, W. Lv, Q. Chang, C. Cui, K. Deng, G. Wang, Q. Dang, S. Wei, Y. Du, & B. Lai, (2022). PP-YOLOE: An evolved version of YOLO. ArXiv, abs/2203.16250.
[38] C. Wang, I. Yeh, & H. Liao, (2021). You Only Learn One Representation: Unified Network for Multiple Tasks. J. Inf. Sci. Eng., 39, 691-709.
[39] S. Elfwing, E. Uchibe, & K. Doya, (2017). Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning. Neural networks : the official journal of the International Neural Network Society, 107, 3-11 .
[40] A.L. Maas, (2013). Rectifier Nonlinearities Improve Neural Network Acoustic Models. |