CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning

TL;DR

This paper introduces CATP, a training-free token pruning method for multimodal in-context learning that significantly reduces image tokens, enhances efficiency, and improves performance across multiple models and benchmarks.

Contribution

We propose a novel, training-free, contextually adaptive token pruning method specifically designed for multimodal in-context learning, addressing redundancy and stability issues.

Findings

Reduces image tokens by 77.8% with a 0.6% performance gain

Decreases inference latency by an average of 10.78%

Outperforms all baseline pruning methods in experiments

Abstract

Modern large vision-language models (LVLMs) convert each input image into a large set of tokens that far outnumber the text tokens. Although this improves visual perception, it also introduces severe image token redundancy. Because image tokens contain sparse information, many contribute little to reasoning but greatly increase inference cost. Recent image token pruning methods address this issue by identifying important tokens and removing the rest. These methods improve efficiency with only small performance drops. However, most of them focus on single-image tasks and overlook multimodal in-context learning (ICL), where redundancy is higher and efficiency is more important. Redundant tokens weaken the advantage of multimodal ICL for rapid domain adaptation and lead to unstable performance. When existing pruning methods are applied in this setting, they cause large accuracy drops, which exposes a clear gap and the need for new approaches. To address this, we propose Contextually Adaptive Token Pruning (CATP), a training-free pruning method designed for multimodal ICL. CATP uses two stages of progressive pruning that fully reflect the complex cross-modal interactions in the input sequence. After removing 77.8% of the image tokens, CATP achieves an average performance gain of 0.6% over the vanilla model on four LVLMs and eight benchmarks, clearly outperforming all baselines. At the same time, it improves efficiency by reducing inference latency by an average of 10.78%. CATP strengthens the practical value of multimodal ICL and lays the foundation for future progress in interleaved image-text settings.