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English(EN) GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

GroundAnything模型通过并行解码实现最先进的视觉定位

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出GroundAnything，一个拥有40亿参数的、用于精确视觉定位的基础模型。该模型采用分块去噪（blockwise denoising）的并行解码方法，与传统的自回归方法形成对比，从而实现更快的处理速度。GroundAnything在30个定位基准测试中取得了最先进的性能，超越了同等规模的模型，并与GPT-6 Astra等更大模型展现出有竞争力的结果。该模型还通过优化的解码策略实现了显著的速度提升，使其适用于实时应用。 AI

影响 实现了更快、更精确的视觉定位，可能改进实时AI应用。

排序理由 发布了一篇详细介绍新型AI模型及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

GroundAnything模型通过并行解码实现最先进的视觉定位

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发布了一篇详细介绍新型AI模型及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qize Yu, Lianrui Fan, Bowen Ping, Xini Ding, Zetian Song, Junbo Niu, Kaixuan Wang, Tianxing Chen, Yue Chen, Minghua He, Yuran Wang, Jie Huang, Haojun Zhang, Min Chen, Hao Li, Wenxuan Song, Ruihai Wu, Xianming Liu, Shilong Liu, Shuchang Zhou, Ping Luo, Sh… ·

    GroundAnything：以闪电般的速度协调并行解码与精确视觉基础

    arXiv:2609.39600v1 Announce Type: cross Abstract: Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidence extraction: objects, locations, and spatial rela…

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