LLM-Guided Agentic Object Detection for Open-World Understanding

Furkan Mumcu; Michael J. Jones; Anoop Cherian; Yasin Yilmaz

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arXiv · 2507.10844

LLM-Guided Agentic Object Detection for Open-World Understanding

Furkan Mumcu·Michael J. Jones·Anoop Cherian·Yasin Yilmaz

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Abstract

Object detection traditionally relies on fixed category sets, requiring costly re-training to handle novel objects. While Open-World and Open-Vocabulary Object Detection (OWOD and OVOD) improve flexibility, OWOD lacks semantic labels for unknowns, and OVOD depends on user prompts, limiting autonomy. We propose an LLM-guided agentic object detection (LAOD) framework that enables fully label-free, zero-shot detection by prompting a Large Language Model (LLM) to generate scene-specific object names. These are passed to an open-vocabulary detector for localization, allowing the system to adapt its goals dynamically. We introduce two new metrics, Class-Agnostic Average Precision (CAAP) and Semantic Naming Average Precision (SNAP), to separately evaluate localization and naming. Experiments on LVIS, COCO, and COCO-OOD validate our approach, showing strong performance in detecting and naming novel objects. Our method offers enhanced autonomy and adaptability for open-world understanding.

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BibTeX RIS

Furkan Mumcu, Michael J. Jones, Anoop Cherian, Yasin Yilmaz. 2025-07-14. LLM-Guided Agentic Object Detection for Open-World Understanding. https://arxiv.org/abs/2507.10844

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