ConsensusDrop: Fusing Visual and Cross-Modal Saliency for Efficient Vision Language Models

TL;DR

ConsensusDrop is a training-free method that fuses visual saliency and cross-modal attention to efficiently reduce tokens in vision-language models, improving performance and efficiency.

Contribution

It introduces a novel consensus-based token pruning framework that combines vision encoder saliency with cross-attention signals without additional training.

Findings

Outperforms prior pruning methods at the same token budget.

Maintains near-baseline accuracy even with aggressive token reduction.

Reduces TTFT and KV cache footprint significantly.

Abstract

Vision-Language Models (VLMs) are expensive because the LLM processes hundreds of largely redundant visual tokens. Existing token reduction methods typically exploit \textit{either} vision-encoder saliency (broad but query-agnostic) \textit{or} LLM cross-attention (query-aware but sparse and costly). We show that neither signal alone is sufficient: fusing them consistently improves performance compared to unimodal visual token selection (ranking). However, making such fusion practical is non-trivial: cross-modal saliency is usually only available \emph{inside} the LLM (too late for efficient pre-LLM pruning), and the two signals are inherently asymmetric, so naive fusion underutilizes their complementary strengths. We propose \textbf{ConsensusDrop}, a training-free framework that derives a \emph{consensus} ranking by reconciling vision encoder saliency with query-aware cross-attention, retaining the most informative tokens while compressing the remainder via encoder-guided token merging. Across LLaVA-1.5/NeXT, Video-LLaVA, and other open-source VLMs, ConsensusDrop consistently outperforms prior pruning methods under identical token budgets and delivers a stronger accuracy-efficiency Pareto frontier -- preserving near-baseline accuracy even at aggressive token reductions while reducing TTFT and KV cache footprint. Our code will be open-sourced.