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

• URL: http://arxiv.org/abs/2605.27365v2
• Date: Wed, 27 May 2026 02:30:49 GMT
• Title: LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
• Abstract summary: We introduce a unified generative grounding and detection framework based on Parallel Box Decoding (PBD)<n>We show that PBD improves both decoding throughput and localization accuracy.<n>We further develop a scalable data engine and curate LocateAnything-Data, a large-scale dataset with more than 138 million training samples.
• Score: 80.49137401046201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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