Transcription of Grounded Language-Image Pre-training
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Grounded Language-Image Pre-training
Grounded Language-Image Pre-trainingLiunian Harold Li 1 , Pengchuan Zhang 2 , Haotian Zhang 3 , Jianwei Yang2, Chunyuan Li2, Yiwu Zhong4 ,Lijuan Wang5, Lu Yuan5, Lei Zhang6, Jenq-Neng Hwang3, Kai-Wei Chang1, Jianfeng Gao21 UCLA,2 Microsoft Research,3 University of Washington,4 University of Wisconsin-Madison,5 Microsoft Cloud and AI,6 International Digital Economy paper presents a Grounded Language-Image Pre-training (GLIP) model for learningobject-level,language-aware, andsemantic-richvisual representations. GLIP uni-fies object detection and phrase grounding for unification brings two benefits: 1) it allows GLIPto learn from both detection and grounding data to im-prove both tasks and bootstrap a good grounding model;2) GLIP can leverage massive image-text pairs by generat-ing grounding boxes in a self-training fashion, making thelearned representations semantic-rich.
Grounded Language-Image Pre-training Liunian Harold Li 1 y, Pengchuan Zhang 2 , Haotian Zhang 3, Jianwei Yang 2, Chunyuan Li , Yiwu Zhong4y, Lijuan Wang 5, Lu Yuan , Lei Zhang6, Jenq-Neng Hwang3, Kai-Wei Chang1, Jianfeng Gao2 1UCLA, 2Microsoft Research, 3University of Washington, 4University of Wisconsin-Madison, 5Microsoft Cloud and AI, 6International Digital …
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