|
百靈佳殷格翰 MMH 計畫(無日期)。【消弭不平等】手語界的維基百科-科技看見了聽障者的聲音。取自https://www.boehringer-ingelheim.tw/making-more-health-3。 莉娜手語工作坊(2006年1月2日)。超有趣手語廣角鏡。取自https://susan6262.pixnet.net/blog/post/287696432。 李信賢(2019年7月8日)。國際手語(IS)是否為一種語言?。取自http://taslifamily.org/?p=4826。 台灣手語(無日期)。维基百科。取自2022年8月1日,https://zh.wikipedia.org/w/index.php?title=%E5%8F%B0%E7%81%A3%E6%89%8B%E8%AA%9E&oldid=68823853。 林雨佑(2020年7月1日)。真的假的?台灣手語跟日本手語嘛會通?。取自https://www.twreporter.org/a/mini-reporter-sign-language-taiwan-and-japan。 王振德(2000年)。國語口手語。載於劉真(主編),教育大辭書(2000年)。台北市:文景。取自https://terms.naer.edu.tw/detail/1309266/。 許逸如(2020年8月21日)。【語言S4E10】台灣手語也有南北差異?!原來台灣手語這麼有趣。取自https://www.mirrormedia.mg/story/20200820cul007/。 蔡素娟、戴浩一、陳怡君(2015年)。【台灣手語線上辭典】第三版中文版.國立中正大學語言學研究所。取自http://tsl.ccu.edu.tw/web/browser.htm SignTube (無日期)。基礎台灣手語[Basic Taiwanese Sign Language]。YouTube。取自2022年8月1日,https://www.youtube.com/playlist?list=PLzI2EvXfsJoM---uqlUP56fENljJ3JWrO albumentations-team. (n.d.). albumentations. Retrieved August 1, 2022, from https://github.com/albumentations-team/albumentations. He, S. (2019, October). Research of a sign language translation system based on deep learning. 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), pp. 392-396. doi: 10.1109/AIAM48774.2019.00083 HHTseng. (n.d.). Video Classification. Retrieved August 1, 2022, from https://github.com/HHTseng/video-classification. Loye, G. (2020, February 10). Gated recurrent unit (GRU) with pytorch. Retrieved from https://blog.floydhub.com/gru-with-pytorch/ SkalskiP. (n.d.). make-sense.ai. Retrieved August 1, 2022, from https://github.com/SkalskiP/make-sense Ultralytics. (n.d.). yolov5. Retrieved August 1, 2022, from https://github.com/ultralytics/yolov5 Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. Chaikaew, A., Somkuan, K., & Yuyen, T. (2021, March). Thai sign language recognition: an application of deep neural network. 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, pp. 128-131. doi: 10.1109/ECTIDAMTNCON51128.2021.9425711 Dima, T. F., & Ahmed, M. E. (2021, July). Using YOLOv5 Algorithm to Detect and Recognize American Sign Language. 2021 International Conference on Information Technology (ICIT), pp. 603-607. doi: 10.1109/ICIT52682.2021.9491672 Farha, Y. A., & Gall, J. (2019). Ms-tcn: Multi-stage temporal convolutional network for action segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3575-3584. Gao, W., Fang, G., Zhao, D., & Chen, Y. (2004). A Chinese sign language recognition system based on SOFM/SRN/HMM. Pattern Recognition, 37(12), 2389-2402. doi: 10.1016/S0031-3203(04)00165-7 Guo, D., Wang, S., Tian, Q., & Wang, M. (2019, August). Dense Temporal Convolution Network for Sign Language Translation. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), pp. 744-750. doi: 10.24963/ijcai.2019/105 Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708. doi: 10.1109/CVPR.2017.243 Hara, K., Kataoka, H., & Satoh, Y. (2018). Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6546-6555. doi: 10.1109/CVPR.2018.00685 He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. doi: 10.1109/CVPR.2016.90 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional Neural Networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386 Koller, O., Zargaran, O., Ney, H., & Bowden, R. (2016). Deep sign: Hybrid CNN-HMM for continuous sign language recognition. Proceedings of the British Machine Vision Conference (BMVC), pp. 136.1-136.12. doi: 10.5244/C.30.136 Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125. Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 156-165. doi: 10.1109/CVPR.2017.113 Lin, J., Gan, C., & Han, S. (2019). Tsm: Temporal shift module for efficient video understanding. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7083-7093. doi: 10.1109/ICCV.2019.00718 Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759-8768. doi: 10.1109/CVPR.2018.00913 Mittal, A., Zisserman, A., & Torr, P. H. (2011, August). Hand detection using multiple proposals. Proceedings of the British Machine Vision Conference, pp.75.1-75.11. doi: 10.5244/C.25.75 Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499. Qiu, Z., Yao, T., & Mei, T. (2017). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision, pp. 5533-5541. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788. doi: 10.1109/CVPR.2016.91 Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), pp. 1137-1149. doi: 10.1109/TPAMI.2016.2577031 Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271. doi: 10.1109/CVPR.2017.690 Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. Slimane, F. B., & Bouguessa, M. (2021, January). Context matters: Self-attention for sign language recognition. 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7884-7891. doi: 10.1109/ICPR48806.2021.9412916 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. doi: 10.1109/CVPR.2015.7298594 Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE international conference on computer vision, pp. 4489-4497. doi: 10.1109/ICCV.2015.510 Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, pp. 6105-6114. Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., & Paluri, M. (2018). A closer look at spatiotemporal convolutions for action recognition. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6450-6459. doi: 10.1109/CVPR.2018.00675 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems 30. Wang, H., Kläser, A., Schmid, C., & Liu, C. L. (2013). Dense trajectories and motion boundary descriptors for action recognition. International journal of computer vision, 103(1), 60-79. doi: 10.1007/s11263-012-0594-8 Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 390-391. doi: 10.1109/CVPRW50498.2020.00203 Wang, H., & Schmid, C. (2013). Action recognition with improved trajectories. Proceedings of the IEEE international conference on computer vision, pp. 3551-3558. doi: 10.1109/ICCV.2013.441 Xie, S., Sun, C., Huang, J., Tu, Z., & Murphy, K. (2018). Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In Ferrari, V., Hebert, M., Sminchisescu, C., & Weiss, Y. (Eds.), Lecture Notes in Computer Science: Vol. 11219. Computer Vision – ECCV 2018 (pp. 305-321). Cham, Switzerland: Springer Nature. doi: 10.1007/978-3-030-01267-0_19 Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015). Beyond short snippets: Deep networks for video classification. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4694-4702. doi: 10.1109/CVPR.2015.7299101 Zhao, K., Zhang, K., Zhai, Y., Wang, D., & Su, J. (2020, July). Real-time sign language recognition based on video stream. 2020 39th Chinese Control Conference (CCC), pp. 7469-7474. doi: 10.23919/CCC50068.2020.9188508
|