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

• URL: http://arxiv.org/abs/2602.00946v1
• Date: Sun, 01 Feb 2026 00:28:55 GMT
• Title: ConsensusDrop: Fusing Visual and Cross-Modal Saliency for Efficient Vision Language Models
• Authors: Dhruv Parikh, Haoyang Fan, Rajgopal Kannan, Viktor Prasanna,
• Abstract summary: Vision-Language Models (VLMs) are expensive because the LLM processes hundreds of largely redundant visual tokens.<n>We show that neither signal alone is sufficient: fusing them consistently improves performance compared to unimodal visual token selection (ranking)<n>We propose textbfConsensusDrop, a training-free framework that derives a emphconsensus ranking by reconciling vision encoder saliency with query-aware cross-attention.
• Score: 4.273730624882391
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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