Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning

Abstract

Abstract: 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 ope

Research Question

What is the impact of dynamic routing overhead on inference latency when adapting vision-language models to new domains, measured through wall-clock time comparisons on ImageNet-1K and COCO captioning benchmarks?

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