# LocateAnything：基于并行框解码的快速高精度视觉语言定位

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-26 08:00
- AIHOT 分数：64
- AIHOT 链接：https://aihot.virxact.com/items/cmpnfrc8m0x75sl01s66bw0d4
- 原文链接：https://arxiv.org/abs/2605.27365

## AI 摘要

LocateAnything 提出了一种统一的生成式视觉定位与检测框架，其核心是并行框解码（PBD）技术。该技术将边界框和点等几何元素作为原子单元一步解码，替代了传统视觉语言模型中串行解码坐标 token 的方式，从而保持了框内几何一致性并实现了大规模并行，显著提升了解码吞吐量与定位精度。研究还构建了包含超过 1.38 亿训练样本的大规模数据集 LocateAnything-Data。评估表明，LocateAnything 在提升解码速度的同时，改善了高交并比（high-IoU）下的定位质量。

## 正文

Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely independently. This token-by-token decoding mismatches the coupled structure of box geometry and creates a practical inference bottleneck due to strictly sequential generation. We introduce LocateAnything, a unified generative grounding and detection framework based on Parallel Box Decoding (PBD). By decoding geometric elements such as bounding boxes and points as atomic units in a single step, LocateAnything preserves intra-box geometric coherence and unlocks substantial parallelism. We show that PBD improves both decoding throughput and localization accuracy. We further develop a scalable data engine and curate LocateAnything-Data, a large-scale dataset with more than 138 million training samples, substantially increasing data diversity for high-precision localization. Extensive evaluations show that LocateAnything advances the speed-accuracy frontier, achieving significantly higher decoding throughput while improving high-IoU localization quality across diverse benchmarks. The results highlight the complementary benefits of Parallel Box Decoding and large-scale training data in enabling efficient and precise unified visual grounding and detection.