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Mamba LLM enhanced with query-based projector for vision-language tasks

Researchers have developed a new query-based cross-modal projector to enhance Mamba-based multimodal LLMs. This projector compresses visual tokens using cross-attention, improving efficiency for vision-language tasks. It also eliminates the need for manual ordering of visual features when inputting them into the Mamba LLM. Experiments show this approach boosts both performance and throughput on various benchmarks. AI

影响 Improves efficiency and performance of vision-language models, potentially enabling more complex multimodal applications.

排序理由 The cluster contains a research paper detailing a new method to improve an existing model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · SooHwan Eom, Jay Shim, Gwanhyeong Koo, Haebin Na, Mark A. Hasegawa-Johnson, Sungwoong Kim, Chang D. Yoo ·

    Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM

    arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the Selective Scan Structured State-Space Model, or Mamba, addresses this computation…