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

GroundAnything模型以4倍速度实现SOTA视觉定位

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-30 00:00

研究人员推出GroundAnything，一款专为视觉定位任务设计的新型40亿参数扩散模型。与串行化空间预测的传统自回归模型相比，该模型利用分块去噪技术实现了更快的并行解码。GroundAnything-VLM，一个自回归变体，在30个定位基准测试中创下了新的SOTA性能，超越了GPT-6 Astra和LocateAnything等模型。该模型还提供了显著的速度提升，其中一个变体在保持竞争性精度的同时，实现了比其自回归对应模型4.51倍的速度提升。 AI

影响在视觉定位基准测试中创下新的SOTA，可能加速需要精确物体定位的实时应用。

排序理由该条目描述了一篇介绍用于视觉定位的新型模型的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-30 00:00

GroundAnything：以闪电般的速度协调并行解码与精确视觉基础

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 relations are jointly constrained by the image and que…

本文如何被排名

报道来源 [1]

GroundAnything：以闪电般的速度协调并行解码与精确视觉基础

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