  Mainstream parameter-efficient fine-tuning (PEFT) methods, such as LoRA or
Adapter, project a model's hidden states to a lower dimension, allowing
pre-trained models to adapt to new data through this low-rank bottleneck.
However, PEFT tasks involving multiple modalities, like vision-language (VL)
tasks, require not only adaptation to new data but also learning the
relationship between different modalities. Targeting at VL PEFT tasks, we
propose a family of operations, called routing functions, to enhance VL
alignment in the low-rank bottlenecks. These feature routing functions adopt
linear operations and do not introduce new trainable parameters. In-depth
analyses are conducted to study their behavior. In various VL PEFT settings,
the routing functions significantly improve performance of the original PEFT
methods, achieving over 20\% improvement on VQAv2
($\text{RoBERTa}_{\text{large}}$+ViT-L/16) and 30\% on COCO Captioning
(GPT2-medium+ViT-L/16). Also when fine-tuning a pre-trained multimodal model
such as CLIP-BART, we observe smaller but consistent improvements across a
range of VL PEFT tasks. Our code is available at
https://github.com/tingyu215/Routing_VLPEFT.
