LRCP prunes visual tokens in LVLMs by scoring projection residuals onto a PCA-estimated low-rank subspace, achieving 88.9% image token reduction with 94.7% performance retention and 87.5% video reduction with 97.8% accuracy retention.
Conical visual concentration for efficient large vision-language models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
baseline 1polarities
baseline 1representative citing papers
ERASE prunes 85% of vision tokens in Qwen2.5-VL-7B while retaining 89.46% accuracy, outperforming prior methods that retain only 78.1%.
citing papers explorer
-
LRCP: Low-Rank Compressibility Guided Visual Token Pruning for Efficient LVLMs
LRCP prunes visual tokens in LVLMs by scoring projection residuals onto a PCA-estimated low-rank subspace, achieving 88.9% image token reduction with 94.7% performance retention and 87.5% video reduction with 97.8% accuracy retention.
-
ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning
ERASE prunes 85% of vision tokens in Qwen2.5-VL-7B while retaining 89.46% accuracy, outperforming prior methods that retain only 78.1%.