arXiv:2605.27365v1 Announce Type: cross 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 bound
The continuous drive for more efficient and performant AI models, especially in vision-language tasks, is pushing researchers to address fundamental bottlenecks like sequential decoding.
This incremental advancement in vision-language grounding directly improves the speed and quality of AI systems that interpret and interact with the visual world, impacting various applications from robotics to content moderation.
The shift from sequential to parallel decoding of visual grounding boxes suggests a fundamental architectural improvement that can lead to faster and potentially more robust vision-language models.
- · AI researchers and developers
- · Robotics companies
- · Computer vision companies
- · AI hardware manufacturers
- · Developers reliant on older sequential decoding methods
Improved performance and efficiency of visual grounding in AI models.
Faster deployment and iteration cycles for vision-language applications in real-world scenarios.
Enhanced capabilities for autonomous agents and robots to understand and interact with complex environments more effectively.
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Read at arXiv cs.LG