Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization

2026-09-02

AI Summary

为解决视觉-语言模型中冗余标记问题，提出CoverPruner方法，通过最大化表示覆盖优化标记剪枝，提高模型在不同压缩率下的平均准确度。

Abstract

Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask which tokens to keep. This retained-token view can keep redundant high-scoring tokens while leaving discarded evidence without a close representative. We propose CoverPruner, a training-free pruner that asks the complementary demand-side question: after a token is removed, which surviving original token represents it for the target VLM? CoverPruner formulates pruning as Representational Coverage Maximization (RCM), covering the full projected visual-token set with query-weighted demand. It instantiates RCM with projector-space coverage and a lightweight first-layer attention probe. Across multiple VLM architectures and compression rates, CoverPruner achieves the best average accuracy among all compared methods, with the largest gains usually appearing under aggressive compression.

Problem

• Visual Token Pruning
• Coverage Optimization
• Representational Coverage Maximization

Innovation

• Representational Coverage Maximization
• Visual Token Pruning
• Coverage Optimization