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English(EN) RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference

RTPrune 通过新颖的令牌剪枝技术将 DeepSeek-OCR 推理速度提升 1.23 倍

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 RTPrune，一种新颖的两阶段令牌剪枝方法，旨在提高 DeepSeek-OCR 推理的效率。该方法模仿了模型两次阅读的过程，首先优先处理高范数令牌以获取显著信息，然后使用最优传输理论合并剩余令牌。RTPrune 还包含针对 OCR 任务定制的动态剪枝比例，实现了准确性和效率之间的卓越平衡。 AI

影响 提高了 OCR 任务的推理速度和效率，可能降低处理长文档的计算成本。

排序理由 这是一篇研究论文，详细介绍了一种优化现有 OCR 模型推理效率的新方法。

在 arXiv cs.CV 阅读 →

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RTPrune 通过新颖的令牌剪枝技术将 DeepSeek-OCR 推理速度提升 1.23 倍

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这是一篇研究论文，详细介绍了一种优化现有 OCR 模型推理效率的新方法。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ben Wan, Yan Feng, Zihan Tang, Weizhe Huang, Yuting Zeng, Jia Wang, Tongxuan Liu ·

    RTPrune：受阅读两次启发的令牌修剪，用于高效 DeepSeek-OCR 推理

    arXiv:2605.00392v1 Announce Type: new Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods fo…

  2. arXiv cs.CV TIER_1 English(EN) · Tongxuan Liu ·

    RTPrune：受阅读两次启发的令牌剪枝，用于高效 DeepSeek-OCR 推理

    DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision-language models (VLMs) fai…

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