做视觉定位或目标检测的开发者，LocateAnything 的并行解码思路能直接提升推理效率，建议关注其开源数据和模型。
LocateAnything 提出并行框解码（PBD）方法，将边界框和点作为原子单元单步解码，替代传统序列化坐标生成，解决了几何结构耦合性差和推理瓶颈问题。该方法在保持框内几何一致性的同时，显著提升解码吞吐量和定位精度。团队还构建了包含1.38亿训练样本的大规模数据集 LocateAnything-Data，增强数据多样性。实验表明，LocateAnything 在多个基准上实现了速度与精度的新前沿，高IoU定位质量显著提升。这项工作展示了并行解码与大规模数据在统一视觉定位与检测中的互补优势。
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
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.