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

Understand

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.