Two papers tackle vision token compression for VLMs
Read original source →arxiv.orgV-CoLA proposes vision token compression built on linear attention, targeting the computational overhead of vision tokens that dominate VLM input sequences. A second paper argues token pruning breaks visual grounding under extreme compression budgets, and instead parameterizes tokens rather than selecting them.
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