LocateAnythingFast and High-Quality Vision-Language Grounding with Parallel Box DecodingABSTRACT:Overcoming Autoregressive Bottlenecks in VLM GroundingVision-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 ...
LocateAnythingFast and High-Quality Vision-Language Grounding with Parallel Box DecodingABSTRACT:Overcoming Autoregressi...
