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Keyword: open-vocabulary object detection ×Clear all

1 claim from 1 paper

1. Computer Science › Advanced Neural Network Applications

   YOLO-World: Real-Time Open-Vocabulary Object Detection

   Cheng, Song, Ge, Liu, Wang and Shan · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

   The paper introduces YOLO-World, which gives the fast YOLO object detector the ability to detect objects from open-ended text descriptions, using vision-language modelling and large-scale pre-training.

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   1. UncheckedOn the LVIS dataset, YOLO-World reports 35.4 AP at 52.0 frames per second on a V100 GPU, which the authors say beats many leading methods in accuracy and speed.“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.”