GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

原文：GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

作者：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, Shiyu Huang

来源：arXiv cs.RO（机器人）

正文

Computer Science > Computer Vision and Pattern Recognition

arXiv:2609.39600v1 (cs)

[Submitted on 30 Sep 2026]

Title:GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

Authors: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, Shiyu Huang

View a PDF of the paper titled GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed, by Qize Yu and 21 other authors

View PDF

HTML (experimental)

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 relations are jointly constrained by the image and query, yet their dependencies do not imply an intrinsic left-to-right generation order. This distinction makes bidirectional diffusion a natural fit, allowing spatial hypotheses to emerge in parallel and be jointly refined through iterative denoising. We introduce GroundAnything, a 4B-parameter grounding foundation model that reconciles fast parallel decoding with precise localization through blockwise denoising. Training combines grounding pretraining from public datasets and dedicated data engines, direct AR-to-diffusion conversion with joint AR and diffusion objectives, supervised fine-tuning, and GRPO-based reinforcement post-training. Across 30 grounding benchmarks, our autoregressive variant, GroundAnything-VLM, establishes a new overall state of the art among similarly sized models at 72.42%, remaining competitive with GPT-6 Astra (71.35%). With entropy-guided decoding, GroundAnything also surpasses the prior state of the art at this scale, averaging 61.75% versus 53.32% for the fast MTP-based LocateAnything model. We further explore decoding strategies, showing that an optional self-speculative mode achieves a $4.51\times$ speedup over the AR counterpart with a 0.74 percentage-point drop in COCO F1mIoU. Infrastructure experiments show that progressive inference optimizations translate parallel decoding into practical speedups. These support efficient visual grounding in latency-sensitive real-world systems.

Comments:

61 pages, including supplementary material. Project page: this https URL Code: [this https URL](this https URL) Model: this https URL, this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)

ACM classes:

I.2.10; I.2.6; I.2.9

Cite as:

arXiv:2609.39600 [cs.CV]

(or

arXiv:2609.39600v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.39600

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

计算机视觉 · 人工智能 · 机器学习 · 机器人

由「前沿雷达」于 2026-09-30 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。