Abstract

This paper presents a grounded language-image pre-training (GLIP) model forlearning object-level, language-aware, and semantic-rich visualrepresentations. GLIP unifies object detection and phrase grounding forpre-training. The unification brings two benefits: 1) it allows GLIP to learnfrom both detection and grounding data to improve both tasks and bootstrap agood grounding model; 2) GLIP can leverage massive image-text pairs bygenerating grounding boxes in a self-training fashion, making the learnedrepresentation semantic-rich. In our experiments, we pre-train GLIP on 27Mgrounding data, including 3M human-annotated and 24M web-crawled image-textpairs. The learned representations demonstrate strong zero-shot and few-shottransferability to various object-level recognition tasks. 1) When directlyevaluated on COCO and LVIS (without seeing any images in COCO duringpre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing manysupervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on valand 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervisedDynamic Head. Code is released at https://github.com/microsoft/GLIP.