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arXiv Machine Learning By Qingchan Zhu, Weihang You, Hanqi Jiang, Changdi Yang, Tianming Liu, Geng Yuan

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

Read the original on arXiv Machine Learning →

CoverPruner is a training‑free visual token pruner that reframes token pruning as a representational coverage maximization problem. Instead of selecting which tokens to keep, it asks which surviving token best represents each removed token for a vision‑language model. Experiments on various VLM architectures show that CoverPruner consistently outperforms existing methods, especially under high compression rates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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