  Adapting large-scale pretrained models to various downstream tasks via
fine-tuning is a standard method in machine learning. Recently,
parameter-efficient fine-tuning methods show promise in adapting a pretrained
model to different tasks while training only a few parameters. Despite their
success, most existing methods are proposed in Natural Language Processing
tasks with language Transformers, and adaptation to Computer Vision tasks with
Vision Transformers remains under-explored, especially for dense vision tasks.
Further, in multi-task settings, individually fine-tuning and storing separate
models for different tasks is inefficient. In this work, we provide an
extensive multi-task parameter-efficient benchmark and examine existing
parameter-efficient fine-tuning NLP methods for vision tasks. Our results on
four different dense vision tasks showed that existing methods cannot be
efficiently integrated due to the hierarchical nature of the Hierarchical
Vision Transformers. To overcome this issue, we propose Polyhistor and
Polyhistor-Lite, consisting of Decomposed HyperNetworks and Layer-wise Scaling
Kernels, to share information across different tasks with a few trainable
parameters. This leads to favorable performance improvements against existing
parameter-efficient methods while using fewer trainable parameters.
Specifically, Polyhistor achieves competitive accuracy compared to the
state-of-the-art while only using ~10% of their trainable parameters.
Furthermore, our methods show larger performance gains when large networks and
more pretraining data are used.
