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

Hugging Face Daily Papers Papers
vision-language visual-grounding object-detection parallel-decoding speed-accuracy large-scale-dataset

Summary

LocateAnything proposes Parallel Box Decoding for unified visual grounding and object detection, decoding geometric elements as atomic units to improve throughput and localization accuracy, supported by a large-scale dataset of 138M samples.

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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Abstract

Parallel Box Decoding enables efficient and accurate unified visual grounding and detection by decoding geometric elements as atomic units, improving both throughput and localization quality.

Vision-language models(VLMs) commonly formulatevisual groundinganddetectionas acoordinate-token generationproblem, serializing each 2D box into multiple 1D tokens that are learned and decoded largely independently. This token-by-token decoding mismatches the coupled structure ofbox geometryand creates a practical inference bottleneck due to strictly sequential generation. We introduce LocateAnything, a unified generative grounding anddetectionframework based onParallel Box Decoding(PBD). By decoding geometric elements such as bounding boxes and points as atomic units in a single step, LocateAnything preserves intra-boxgeometric coherenceand unlocks substantial parallelism. We show that PBD improves bothdecoding throughputandlocalization 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 higherdecoding throughputwhile improving high-IoU localization quality across diverse benchmarks. The results highlight the complementary benefits ofParallel Box Decodingandlarge-scale training datain enabling efficient and precise unifiedvisual groundinganddetection.

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