Learning Transferable Feature Representation with Swin Transformer for Object Recognition-Reference-Cited by-同舟云学术

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Learning Transferable Feature Representation with Swin Transformer for Object Recognition

Published:2022-08-27 Issue: Volume: Page:
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Ren Jian-Xin,Xiong Yu-Jie,Xie Xi-Jiong,Dai Yu-Fan

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Science and Technology Innovation Plan Of Shanghai Science and Technology Commission

Youth and Middle-aged Scientific and Technological Innovation Leading Talents Program of the Corps

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-11004-3.pdf

Reference26 articles.

1. Kang Y, Chao G, Hu X, Tu Z, Chu D (2022) Deep learning for fine-grained image recognition: a comprehensive study. In: 2022 4th Asia pacific information technology conference, pp 31–39

2. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

3. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

4. Deng Z, Zhou L (2018) Detection and recognition of traffic planar objects using colorized laser scan and perspective distortion rectification. IEEE Trans Intell Transp Syst 19(5):1485–1495

5. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507

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