title: YOLO-World: Real-Time Open-Vocabulary Object Detection
summary: 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.
paper_authors._id: 65b9be7ef055699cacf3750c
paper_authors.name: Tianheng Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:32:50.040Z
paper_authors._id: 646b3db131968a60a01e4cf5
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paper_authors.fullname: Tianheng Cheng
paper_authors.type: user
paper_authors.user: wondervictor
paper_authors._id: 65b9be7ef055699cacf3750d
paper_authors.name: Lin Song
paper_authors._id: 65b9be7ef055699cacf3750e
paper_authors.name: Yixiao Ge
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:33:40.610Z
paper_authors._id: 640e9762b03f4cd29f58d982
paper_authors.avatarUrl: /avatars/81da37d628163fe3e094b247c7c3a3b5.svg
paper_authors.fullname: Yixiao Ge
paper_authors.type: user
paper_authors.user: yxgeee
paper_authors._id: 65b9be7ef055699cacf3750f
paper_authors.name: Wenyu Liu
paper_authors._id: 65b9be7ef055699cacf37510
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:34:29.624Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_authors._id: 65b9be7ef055699cacf37511
paper_authors.name: Ying Shan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:34:39.770Z
paper_authors._id: 63ca3ddc04c979828310bfcb
paper_authors.avatarUrl: /avatars/615e0d8622950b4408b40d550f02a894.svg
paper_authors.fullname: Ying Shan
paper_authors.type: user
paper_authors.user: yshan2u
paper_title: YOLO-World: Real-Time Open-Vocabulary Object Detection
paper_summary: 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.
paper_ai_summary: YOLO-World enhances YOLO with open-vocabulary detection using vision-language modeling and a re-parameterizable network, achieving high accuracy and speed.
paper_ai_keywords: YOLO-World open-vocabulary detection vision-language modeling Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) region-text contrastive loss LVIS dataset zero-shot manner object detection open-vocabulary instance segmentation