Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
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Computer Science > Computer Vision and Pattern Recognition
Title:Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization
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.
| Comments: | Accepted to EMNLP 2026 main |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.03158 [cs.CV] |
| (or arXiv:2609.03158v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.03158
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