Visual Prompting via Partial Optimal Transport

Visual prompts represent a lightweight approach that adapts pre-trained models to downstream tasks without modifying the model parameters. They strategically transform the input and output through prompt engineering and label mapping, respectively. Yet, existing methodologies often overlook the synergy between these components, leaving the intricate relationship between them underexplored. To address this, we propose an Optimal Transport-based Label Mapping strategy (OTLM) that effectively reduces distribution migration and lessens the modifications required by the visual prompts. Specifically, we reconceptualize label mapping as a partial optimal transport problem, and introduce a novel transport cost matrix. Through the optimal transport framework, we establish a connection between output-side label mapping and input-side visual prompting. Additionally, we analyze frequency-based label mapping methods within this framework. We also offer an analysis of frequency-based label mapping techniques and demonstrate the superiority of our OTLM method. Our experiments across multiple datasets and various model architectures demonstrate significant performance improvements, which prove the effectiveness of the proposed method.