While parameter efficient tuning (PET) methods have shown great potential
with transformer architecture on Natural Language Processing (NLP) tasks, their
effectiveness is still under-studied with large-scale ConvNets on Computer
Vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for
ConvNets. Conv-Adapter is light-weight, domain-transferable, and
architecture-agnostic with generalized performance on different tasks. When
transferring on downstream tasks, Conv-Adapter learns tasks-specific feature
modulation to the intermediate representations of backbone while keeping the
pre-trained parameters frozen. By introducing only a tiny amount of learnable
parameters, e.g., only 3.5% full fine-tuning parameters of ResNet50,
Conv-Adapter outperforms previous PET baseline methods and achieves comparable
or surpasses the performance of full fine-tuning on 23 classification tasks of
various domains. It also presents superior performance on few-shot
classifications, with an average margin of 3.39%. Beyond classification,
Conv-Adapter can generalize to detection and segmentation tasks with more than
50% reduction of parameters but comparable performance to the traditional full
fine-tuning.

本文提出了 Conv-Adapter，一种适用于 CV 的参数高效调整方法，通过学习特定任务的高效特征调整使得在各种分类下游任务上表现出色，且具有与传统方法相当的性能，同时适用于检测和分割任务。