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pro vyhledávání: '"Xu, Xuwei"'
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational demands. To expedite pre-trained ViTs, token pruning and token merging app
Externí odkaz:
http://arxiv.org/abs/2311.03035
Conventionally, during the knowledge distillation process (e.g. feature distillation), an additional projector is often required to perform feature transformation due to the dimension mismatch between the teacher and the student networks. Interesting
Externí odkaz:
http://arxiv.org/abs/2310.17183
Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resource-constrained environments. Various token pruning techniques have bee
Externí odkaz:
http://arxiv.org/abs/2310.05654
Vision Transformers (ViTs) have demonstrated remarkable performance in various computer vision tasks. However, the high computational complexity hinders ViTs' applicability on devices with limited memory and computing resources. Although certain inve
Externí odkaz:
http://arxiv.org/abs/2310.05642
In knowledge distillation, previous feature distillation methods mainly focus on the design of loss functions and the selection of the distilled layers, while the effect of the feature projector between the student and the teacher remains under-explo
Externí odkaz:
http://arxiv.org/abs/2210.15274
Publikováno v:
In Computers and Geosciences January 2024 182
Publikováno v:
In ISPRS Journal of Photogrammetry and Remote Sensing September 2022 191:203-222
Autor:
Xu, Xuwei1,2 (AUTHOR), Zhou, Yuan3 (AUTHOR), Lu, Xiechun4 (AUTHOR), Chen, Zhanlong1,2,3,5 (AUTHOR) chenzl@cug.edu.cn
Publikováno v:
Remote Sensing. Jan2023, Vol. 15 Issue 2, p395. 24p.
Akademický článek
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