Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs
Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization has become a dominant strategy for token reduction, existing methods rely on cosine-based normalized similarity that discards magnitude information, failing to faithfully approximate the original feature representation and leading to suboptimal performance, particularly on compositional multi-skill reasoning tasks. In this paper, we introduce SPARE, a subspace reconstruction method that reformulates token pruning as a column subset selection problem and explicitly minimizes reconstruction error. By iteratively s...
| Authors | Jaeyeon Lee, Shunjie Wen, Dong-Wan Choi |
|---|---|
| Primary category | cs.CV |
| Published | 17 June 2026 |
| Last updated at source | 24 July 2026 |
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