BriefGPT.xyz
Oct, 2023
稠密视觉Transformer的选择性特征适配器
Selective Feature Adapter for Dense Vision Transformers
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Xueqing Deng, Qi Fan, Xiaojie Jin, Linjie Yang, Peng Wang
TL;DR
本文提出了一种有效的方法,即选择性特征适配器(SFA),以解决精细调整预训练变压器模型中庞大的参数的成本/存储问题,并在各种密集任务中实现了最先进的性能,比其他适配器模块更出色。
Abstract
fine-tuning
pre-trained
transformer models
, e.g., Swin Transformer, are successful in numerous downstream for
dense prediction vision tasks
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