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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. arXiv preprint arXiv:2102.12122, 2021."},{"key":"e_1_3_2_1_4_1","volume-title":"An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy , Lucas Beyer , Alexander Kolesnikov , Dirk Weissenborn , Xiaohua Zhai , Thomas Unterthiner , Mostafa Dehghani , Matthias Minderer , Georg Heigold , Sylvain Gelly , An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 , 2020 . Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Sun C Shrivastava A Singh S Revisiting unreasonable effectiveness of data in deep learning era[C]\/\/Proceedings of the IEEE international conference on computer vision. 2017: 843-852.  Sun C Shrivastava A Singh S Revisiting unreasonable effectiveness of data in deep learning era[C]\/\/Proceedings of the IEEE international conference on computer vision. 2017: 843-852.","DOI":"10.1109\/ICCV.2017.97"},{"key":"e_1_3_2_1_6_1","volume-title":"Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes","author":"Liao Minghui","year":"2019","unstructured":"Minghui Liao , Pengyuan Lyu , Minghang He , Cong Yao , Wenhao Wu , and Xiang Bai . Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes . IEEE transactions on pattern analysis and machine intelligence, 2019 . Minghui Liao, Pengyuan Lyu, Minghang He, Cong Yao, Wenhao Wu, and Xiang Bai. Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. 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Yu D, Li X, Zhang C, Towards accurate scene text recognition with semantic reasoning networks[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 12113-12122.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_14_1","volume-title":"Cvt: Introducing convolutions to vision transformers. arXiv preprint arXiv:2103.15808","author":"L.","year":"2021","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. ( 2021 ). Cvt: Introducing convolutions to vision transformers. arXiv preprint arXiv:2103.15808 . Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021). Cvt: Introducing convolutions to vision transformers. arXiv preprint arXiv:2103.15808."},{"key":"e_1_3_2_1_15_1","volume-title":"Do we really need explicit position encodings for vision transformers? arXiv preprint arXiv:2102.10882","author":"Chu Xiangxiang","year":"2021","unstructured":"Xiangxiang Chu , Bo Zhang , Zhi Tian , Xiaolin Wei , and Huaxia Xia . Do we really need explicit position encodings for vision transformers? arXiv preprint arXiv:2102.10882 , 2021 . Xiangxiang Chu, Bo Zhang, Zhi Tian, Xiaolin Wei, and Huaxia Xia. Do we really need explicit position encodings for vision transformers? arXiv preprint arXiv:2102.10882, 2021."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.  He K Zhang X Ren S Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_17_1","volume-title":"Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556","author":"Simonyan","year":"2014","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556 , 2014 . Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_18_1","volume-title":"labelling unsegmented sequence data with recurrent neural networks.\" Proceedings of the 23rd international conference on Machine learning","author":"Connectionist","year":"2006","unstructured":"Graves, Alex, \" Connectionist temporal classification : labelling unsegmented sequence data with recurrent neural networks.\" Proceedings of the 23rd international conference on Machine learning . 2006 . Graves, Alex, \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" Proceedings of the 23rd international conference on Machine learning. 2006."},{"volume-title":"A practical ultra lightweight ocr system.\" arXiv preprint arXiv:2009.09941","year":"2020","key":"e_1_3_2_1_19_1","unstructured":"Du, Yuning, \"Pp-ocr : A practical ultra lightweight ocr system.\" arXiv preprint arXiv:2009.09941 ( 2020 ). Du, Yuning, \"Pp-ocr: A practical ultra lightweight ocr system.\" arXiv preprint arXiv:2009.09941 (2020)."},{"key":"e_1_3_2_1_20_1","volume-title":"ADADELTA: An Adaptive Learning Rate Method[J]. arXiv e-prints","author":"Zeiler MD","year":"2012","unstructured":"MD Zeiler . ADADELTA: An Adaptive Learning Rate Method[J]. arXiv e-prints , 2012 . MD Zeiler. 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