Abstract
Transformers have recently lead to encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs: (i) a linear complexity attention layer, (ii) an overlapping patch embedding, and (iii) a convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification, detection, and segmentation. In particular, PVT v2 achieves comparable or better performance than recent work such as the Swin transformer. We hope this work will facilitate state-of-the-art transformer research in computer vision. Code is available at https://github.com/whai362/PVT.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61672273 and 61832008, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant No. BK20160021, the Postdoctoral Innovative Talent Support Program of China under Grant Nos. BX20200168 and 2020M681608, and the General Research Fund of Hong Kong under Grant No. 27208720.
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Wenhai Wang received his B.S. degree from Nanjing University of Science and Technology, China, in 2016. He is currently a Ph.D. student with the Department of Computer Science, Nanjing University. His main research interests include scene text detection and recognition, deep neural network exploration, object detection, and instance segmentation.
Enze Xie received his B.S. degree from Nanjing University of Aeronautics and Astronautics, China, in 2016, and his M.S. degree from Tongji University, China, in 2019. He is currently a Ph.D. student with the Department of Computer Science, the University of Hong Kong. His main research interests include object detection and instance segmentation.
Xiang Li received his B.S. degree in computer science from Nanjing University of Science and Technology in 2013, where he is currently working towards a Ph.D. degree in pattern recognition and intelligent systems. His research interests include computer vision, pattern recognition, data mining, and deep learning.
Deng-Ping Fan is a postdoctoral researcher at ETH Zurich, Switzerland. He received his Ph.D. degree from Nankai University in 2019. He joined the Inception Institute of Artificial Intelligence (IIAI) in 2019. He has published about 30+ top journal and conference papers. His research interests include computer vision, deep learning, and saliency detection.
Kaitao Song received his Ph.D. degree in computer science from Nanjing University of Science and Technology in 2021. His research interests focus on machine learning and deep learning algorithms for natural language processing and speech processing, including pretrained language models, neural machine translation, music generation, text summarization, neural architecture search for NLP, audio speech recognition, text-to-speech synthesis, etc.
Ding Liang has been working for SenseTime Ltd., since he graduated from Tsinghua University. He is now an associate director and head of the OCR team. His main research interests include OCR, face recognition, and model compression.
Tong Lu received his Ph.D. degree in computer science from Nanjing University in 2005, where he also received his M.Sc. and B.Sc. degrees in 2002 and 1997, respectively. He served as associate professor and assistant professor in the Department of Computer Science and Technology at Nanjing University from 2007 and 2005, respectively, where he is now a full professor. He is also a member of the National Key Laboratory of Novel Software Technology in China. He has published over 130 papers and authored 2 books, and received more than 30 international and Chinese patents. His current interests are in multimedia, computer vision, and pattern recognition algorithms and systems.
Ping Luo is an assistant professor in the Department of Computer Science, The University of Hong Kong. He received his Ph.D. degree in 2014 from Information Engineering, the Chinese University of Hong Kong, and was a postdoctoral fellow there from 2014 to 2016. He joined SenseTime Research as a principal research scientist from 2017 to 2018. His research interests are machine learning and computer vision. He has published 100+ peer-reviewed articles in top-tier conferences and journals. He was named a young innovator under 35 by MIT Technology Review (TR35) Asia Pacific.
Ling Shao is the CEO and Chief Scientist of the Inception Institute of AI (IIAI), Abu Dhabi, United Arab Emirates (UAE). He was the initiator and Founding Provost and Executive Vice President of the Mohamed bin Zayed University of Artificial Intelligence (the world’s first AI University), UAE. His research interests include computer vision, machine learning, and medical imaging. He is a fellow of the IEEE, the IAPR, the IET, and the BCS.
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Wang, W., Xie, E., Li, X. et al. PVT v2: Improved baselines with Pyramid Vision Transformer. Comp. Visual Media 8, 415–424 (2022). https://doi.org/10.1007/s41095-022-0274-8
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DOI: https://doi.org/10.1007/s41095-022-0274-8