YOLOA：通过LLM适配器实现实时的可及性检测
YOLOA: Real-Time Affordance Detection via LLM Adapter
AI总结：
YOLOA通过LLM适配器实现实时可及性检测，结合对象检测与可及性学习，取得高精度与高效率的平衡。
摘要：
可及性检测旨在通过理解“是什么”、“在哪里”和“如何使用”来共同解决具身AI中的根本“是什么-在哪里-如何”挑战。然而，大多数可及性学习方法仅关注“如何”使用对象，而忽视了“是什么”和“在哪里”方面。其他可及性检测方法将对象检测和可及性学习视为两个独立的任务，缺乏有效的交互和实时能力。为克服这些限制，我们引入了YOLO可及性（YOLOA），一种能够实时进行可及性检测的模型，通过大型语言模型（LLM）适配器共同处理这两个任务。具体而言，YOLOA采用了一个轻量级的检测器，由对象检测和可及性学习分支组成，通过LLM适配器进行优化。在训练过程中，LLM适配器与对象和可及性初步预测进行交互，通过生成更准确的类别先验、框偏移和可及性门控来优化两个分支。在我们重新标记的ADG-Det和IIT-Heat基准测试中，实验表明，YOLOA在ADG-Det和IIT-Heat上分别达到52.8 / 73.1 mAP的最先进精度，同时保持实时性能（最高89.77 FPS，轻量级变体最高846.24 FPS）。这表明，YOLOA在精度和效率之间实现了优秀的权衡。
英文摘要：
Affordance detection aims to jointly address the fundamental "what-where-how" challenge in embodied AI by understanding "what" an object is, "where" the object is located, and "how" it can be used. However, most affordance learning methods focus solely on "how" objects can be used while neglecting the "what" and "where" aspects. Other affordance detection methods treat object detection and affordance learning as two independent tasks, lacking effective interaction and real-time capability. To overcome these limitations, we introduce YOLO Affordance (YOLOA), a real-time affordance detection model that jointly handles these two tasks via a large language model (LLM) adapter. Specifically, YOLOA employs a lightweight detector consisting of object detection and affordance learning branches refined through the LLM Adapter. During training, the LLM Adapter interacts with object and affordance preliminary predictions to refine both branches by generating more accurate class priors, box offsets, and affordance gates. Experiments on our relabeled ADG-Det and IIT-Heat benchmarks demonstrate that YOLOA achieves state-of-the-art accuracy (52.8 / 73.1 mAP on ADG-Det / IIT-Heat) while maintaining real-time performance (up to 89.77 FPS, and up to 846.24 FPS for the lightweight variant). This indicates that YOLOA achieves an excellent trade-off between accuracy and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
