LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions

TL;DR

This paper introduces MDPR, a dynamic prompt routing framework that leverages a multi-dimensional knowledge base to improve vision-language model fine-tuning on long-tailed, class-imbalanced datasets, achieving state-of-the-art results.

Contribution

The paper proposes a novel multi-dimensional dynamic prompt routing method that effectively addresses class imbalance in VLM fine-tuning by utilizing a semantic knowledge base and dynamic routing.

Findings

MDPR achieves comparable results with SOTA on long-tailed benchmarks.

Semantic library improves tail class performance.

Minimal computational overhead from dynamic routing.

Abstract

Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive capability in visual tasks, but their fine-tuning often suffers from bias in class-imbalanced scene. Recent works have introduced large language models (LLMs) to enhance VLM fine-tuning with supplementing semantic information. However, they often overlook inherent class imbalance in VLMs' pre-training, which may lead to bias accumulation in downstream tasks. To address this problem, this paper proposes a Multi-dimensional Dynamic Prompt Routing (MDPR) framework. MDPR constructs a comprehensive knowledge base for classes, spanning five visual-semantic dimensions. During fine-tuning, the dynamic routing mechanism aligns global visual classes, retrieves optimal prompts, and balances fine-grained semantics, yielding stable predictions through logits fusion. Extensive experiments on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT, and Places-LT, demonstrate that MDPR achieves comparable results with current SOTA methods. Ablation studies further confirm the effectiveness of our semantic library for tail classes, and show that our dynamic routing incurs minimal computational overhead, making MDPR a flexible and efficient enhancement for VLM fine-tuning under data imbalance.