NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning

Understand

In this paper, we consider the problem of prototype-based vision-language reasoning problem.

• We observe that existing methods encounter three major challenges: 1) escalating resource demands and prolonging training times, 2) contending with excessive learnable parameters, and 3) fine-tuning based only on a single modality.
• These challenges will hinder their capability to adapt Vision-Language Models (VLMs) to downstream tasks.
• Motivated by this critical observation, we propose a novel method called NODE-Adapter, which utilizes Neural Ordinary Differential Equations for better vision-language reasoning.