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

LocateAnything is a new framework for vision-language grounding and detection that uses Parallel Box Decoding (PBD) to improve both speed and accuracy. Unlike traditional methods that decode 2D boxes token by token, PBD decodes geometric elements as atomic units in a single step, enhancing parallelism and preserving geometric coherence. The framework is supported by LocateAnything-Data, a large dataset with over 138 million training samples.

RDR83Confidence 85%vision-language modelsvisual groundingobject detectionparallel processingdeep learningcomputer vision

Why it matters

This research introduces a novel approach to visual grounding and detection that addresses the inference bottleneck of current vision-language models. By enabling faster and more accurate localization, LocateAnything could significantly impact applications requiring precise object detection and understanding, such as robotics, autonomous systems, and advanced image analysis tools.

Researchers have introduced LocateAnything, a unified generative framework for vision-language grounding and detection. The core innovation is Parallel Box Decoding (PBD), which processes geometric elements like bounding boxes and points as single, atomic units rather than a sequence of independent tokens. This method contrasts with conventional vision-language models (VLMs) that serialize 2D boxes into multiple 1D tokens, leading to sequential generation bottlenecks and potential loss of geometric coherence.

PBD is designed to improve both decoding throughput and localization accuracy by leveraging parallelism. The framework also includes LocateAnything-Data, a large-scale dataset comprising over 138 million training samples. This extensive dataset aims to increase data diversity, which is crucial for achieving high-precision localization. Evaluations indicate that LocateAnything significantly advances the speed-accuracy frontier, offering higher decoding throughput and improved high-IoU localization quality across various benchmarks. The findings emphasize the combined benefits of Parallel Box Decoding and large-scale training data for efficient and precise unified visual grounding and detection.