Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

Vision-language models (VLMs) commonly formulate visualgrounding and detection as a coordinate-token generation problem, serial-izing each 2D box into multiple 1D tokens that are learned and decodedlargely independently. This token-by-token decoding mismatches the cou-pled structure of box geometry and creates a practical inference bottleneckdue to strictly sequential generation. We introduce LocateAnything,a unified generative grounding and detection framework based on Par-allel Box Decoding (PBD). By decoding geometric elements such asbounding boxes and points as atomic units in a single step, LocateAny-thing preserves intra-box geometric coherence and unlocks substantialparallelism. We show that PBD improves both decoding throughput andlocalization accuracy. We further develop a scalable data engine andcurate LocateAnything-Data, a large-scale dataset with more than138 million training samples, substantially increasing data diversity forhigh-precision localization. Extensive evaluations show that LocateAny-thing advances the speed–accuracy frontier, achieving significantly higherdecoding throughput while improving high-IoU localization quality acrossdiverse benchmarks. The results highlight the complementary benefits ofParallel Box Decoding and large-scale training data in enabling efficientand precise unified visual grounding and detection.