YOLO-World: Real-Time Open-Vocabulary Object Detection
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.
65b9be7ef055699cacf3750c
Tianheng Cheng
admin_assigned
2024-01-31T09:32:50.040Z
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Tianheng Cheng
user
wondervictor
65b9be7ef055699cacf3750d
Lin Song
65b9be7ef055699cacf3750e
Yixiao Ge
admin_assigned
2024-01-31T09:33:40.610Z
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Yixiao Ge
user
yxgeee
65b9be7ef055699cacf3750f
Wenyu Liu
65b9be7ef055699cacf37510
Xinggang Wang
admin_assigned
2024-01-31T09:34:29.624Z
62600de6d47e3dbae32ce1ce
/avatars/a536417cfec6e10ac415091bd1829426.svg
Xinggang Wang
user
xinggangw
65b9be7ef055699cacf37511
Ying Shan
admin_assigned
2024-01-31T09:34:39.770Z
63ca3ddc04c979828310bfcb
/avatars/615e0d8622950b4408b40d550f02a894.svg
Ying Shan
user
yshan2u
YOLO-World: Real-Time Open-Vocabulary Object Detection
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.
YOLO-World enhances YOLO with open-vocabulary detection using vision-language modeling and a re-parameterizable network, achieving high accuracy and speed.
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