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在蒙面图像建模中探索频率和注意力的协调

Jie Gui, Tuo Chen, Minjing Dong

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|August 8, 2025

概括

频率和注意力驱动的掩盖和投策略 (FAMT) 通过智能掩盖图像补丁来增强自我监督的学习,将训练时间减少50%并提高准确性.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

蒙面图像建模 (MIM) 是计算机视觉中的一个关键的自我监督学习技术.
目前的MIM方法由于随机补丁掩盖和大数据集而遭受长时间的预训练时间.
随机掩盖无法利用语义信息,阻碍了有效的视觉表示学习.

研究的目的:

为了提高蒙面图像建模的效率和性能.
开发一种利用语义信息进行更有效的视觉表现学习的策略.
为了降低与MIM预培训相关的计算成本.

主要方法:

提议的频率和注意力驱动的掩护和投策略 (FAMT).
利用自我注意力和频率域信息来识别和掩盖语义图像补丁.
实施了贴片投策略,以减少训练贴片的数量.

主要成果:

在各种数据集中减少了近50%的培训时间.
蒙面自动编码器 (MAE) 的线性探测精度提高了1.3%至3.9%.
在下游检测和细分任务中表现出卓越的性能.

结论:

FAMT有效地提取语义补丁,提高模型性能和训练效率.
插即用FAMT模块显著增强了现有的MIM方法.
FAMT为高效和有效的自我监督视觉表示学习提供了一个有希望的方向.

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