The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation.

翻译：你只看一次（YOLO）系列检测器已成为高效且实用的工具。然而，其对预定义和训练类别对象的依赖限制了其在开放场景中的适用性。为解决这一局限，我们提出YOLO-World——一种创新方法，通过视觉-语言建模和大规模数据集预训练，赋予YOLO开放词汇检测能力。具体而言，我们提出了新型可重参数化视觉-语言路径聚合网络（RepVL-PAN）和区域-文本对比损失，以促进视觉与语言信息的交互。该方法能够以零样本方式高效检测广泛类别的对象。在具有挑战性的LVIS数据集上，YOLO-World在V100上达到35.4 AP和52.0 FPS，在精度和速度上均超越多项现有最优方法。此外，微调后的YOLO-World在目标检测和开放词汇实例分割等多个下游任务中展现出卓越性能。